Author Archives: svedic

Two Rules of AI Business and Startups That Ignore Them

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These rules are not new, and they are not mine; I stole them from Andrew Ng and Benedict Evans, two men with a huge following. Still, a large majority of AI entrepreneurs and engineers don’t pay attention to them, maybe because these rules show why their AI project will fail.

AI’s Law of Diminishing Returns

To paraphrase Andrew’s words from Coursera’s Deep Learning Specialization course:

The effort to half an AI system’s error rate is similar, regardless of the starting error rate. 

This is not very intuitive. If an AI system passes 90% of test cases and errors on 10%, then you are 90% done, right? Fix the remaining 10% of errors, and you will have 100% accuracy? Absolutely not. If it took you six months to halve the error rate from 20% to 10%, it will take you approximately another six months to halve 10% to 5%. And another six months to halve 5% to 2.5%. Ad infinitum. You will never achieve a 0% error rate on a real-world AI system. For an illustrative example, see this typical chart of error rate vs the number of training samples:

Notice that later in the training process, training set size increases exponentially with each error rate halving, and the error rate never reaches zero. Sure, you will get more efficient with acquiring training data (e.g., by using low-quality sources or synthetic data). Still, it is hard to believe that acquiring 10X more data is going to be much easier than acquiring the initial set. 

This rule becomes more intuitive when dissecting what an AI system error rate represents: uncovered real-world special cases. There are an infinite number of them. For example, one of the easiest machine learning (ML) tasks is classifying images of dogs and cats. It is an introductory task with online tutorials that get 99% accuracy. But solving the last 1% is incredibly hard. For example, is the creature in the image below a dog or a cat?

It is Atchoum the cat, who rose to fame because half of humans recognized him as a dog. The human accuracy on dog/cat classification within 30 seconds is 99.6%. A dog/cat classifier with less than a 0.4% error rate would be superhuman. But it is possible. A training set with hundreds of thousands of strange-looking dogs and cats would teach a neural network to focus just on details encoded in dog or cat chromosomes (e.g. cat eyes). However, building such a dataset is orders of magnitude more complex than a tutorial with 99% accuracy. Other problems lurk in that 1% error rate: photos that are too dark, photos in low resolution, photo compression artifacts, photo post-processing by modern smartphones (adding of non-existing details), dogs and cats with medical conditions etc. The problem space is infinite. This is still considered a solved ML problem though, because a 1% error rate is low enough for all practical purposes. 

But for some problems, even a 0.01% error rate is not satisfactory, for example: full-self driving (FSD). Elon Musk said in a 2015 article with Forbes:

“We’re going to end up with complete autonomy, and I think we will have complete autonomy in approximately two years.”

Tesla was so confident in that prediction that they started selling a full self-driving add-on package in 2016, and they weren’t the only ones. Kyle Vogt, CEO of Cruise, wrote a piece called How we built the first real self-driving car (really) in 2017, in which he claimed:

“the most critical requirement for deployment at scale is actually the ability to manufacture the cars that run that software”

So, the software and the working prototype are done; they just need to mass-produce “100,000 vehicles per year.” 

Fast forward to 2024. Elon Musk’s predictions for autonomous Tesla vehicles deserved a lengthy Wikipedia table, mostly in red: 

What about Kyle Vogt? In October of 2023 Cruise’s car dragged a pedestrian for 20 feet, after which California’s DMV suspended Cruise’s self-driving taxi license. Kyle “resigned” as CEO in November 2023.  

Don’t misunderstand me—I believe autonomous cars will have a significant market share, probably in the next decade. The failed predictions above illustrate what happens when entrepreneurs don’t respect the AI law of diminishing returns. Elon and Kyle probably saw a demo of a full-self driving car that could drive on its own, on a sunny day, on a marked road. Sure, a safety driver needed to intervene sometimes, but that was only 1% of the drive time. It is easy to conclude that “autonomous driving is a solved problem,” as Elon said in 2016. Notice how ML scientists and engineers didn’t make such bombastic claims. They were aware of many edge cases, some of which are described in crash reports. Edge cases include:

Why so many companies promised a drastic reduction in self-driving error rates in such a short time without having a completely new ML architecture is an open question. Scaling laws for convolutional neural networks have been known for some time, and the new transformer architecture obeys a similar scaling law. 

AI’s Product vs Feature Rule

When is an AI system a good stand-alone product, and when is it just a feature? In the words of Benedict Evans from The AI Summer podcast: “Is this feature or a product? Well, if you can’t guarantee it is right, it’s a feature. It needs to be wrapped in something that manages or controls expectations.” I love that statement. The “it is right” part can be broken down using error rate:

If your AI system has a higher error rate than target users, you have an AI feature in an existing workflow, not a stand-alone AI product.

This rule is more intuitive than the law of diminishing returns. If target users are better at a task, they will not like stand-alone AI system results. They could still use AI to save them effort and time, but they will want to review and edit AI output. If AI completely fails at a task, humans will use the old workflow and the old software to finish the task.

Let’s take MidJourney for example, which generates whole images based on a text prompt. When I used it for a hobby project last year, satisfying artistic images appeared instantly, like magic. But then I spent hours fixing creepy hands, similar to the ones below:

Each time MidJourney created a new image, one of the hands had strange artifacts. Finally, it generated an image with two normal hands—but then it destroyed the ears in another part of the image. The problem was less with wrong details and more with bad UI, which didn’t allow correction of the AI’s mistakes.

Adobe’s approach is different—it treats generative AI as just one feature in its product suite. You use an existing tool, select an area, and then do a generative fill:

You can use it for the smallest of tasks, like removing cigarette butts from grass in a wedding photoshoot. If you dislike AI grass, no problem—revert to the old designer joy of manually cloning grass. Also, Adobe Illustrator has generative Vector AI that generates vector shapes you can edit to your liking.

MidJourney makes more impressive demos, but Adobe’s approach is more useful to professional designers. That doesn’t mean MidJourney doesn’t make sense as a product, its target users are the ones who don’t care about details. For example, last Christmas, I got the following greetings image over WhatsApp:

Did you notice baby Jesus’ hands and eyes? Take another look:

That would never pass with a designer, but that is not the point. There is a whole army of users who don’t care about image composition and details, they just want images that go with their content. In other words, MidJourney is not a replacement for Adobe’s Creative Suite—it is a replacement for stock photo libraries like Shutterstock and Getty Images. And judging by the recent popularity of AI-generated images on social media and the web, people like artsy MidJourney images more than stock photos.

Low-hanging fruit in stand-alone AI products are use cases where a high error rate doesn’t matter or is still better than the human error rate. An unfortunate example is guided missiles; in the Gulf War, the accuracy of Tomahawk missiles was less than 60%. But the army was happy to buy Tomahawks because they were still much more accurate than older alternatives, as fewer than 1 in 14 unguided bombs hit their targets.

Evaluating startups based on the above rules

The great thing is that error rates are measurable, so the above rules give a framework to judge an AI startup quickly. Below is a simple startup example.

Devin AI made quite a splash in March of 2024 with a video demo of developer AI that can create fully working software projects. The announcement says that Devin was “evaluated on the SWE-Bench” (relevant benchmark), and “correctly resolves 13.86% of the issues unassisted, far exceeding the previous state-of-the-art model performance of 1.96% unassisted.” So, the current state-of-the-art (SOTA) has a 98% error rate, and they claim to have an 86% error rate. Even if that claim is valid (it wasn’t independently verified), why do their promo videos show success after success? It turns out that the video examples were cherry-picked, the task description was changed, and Devin took hours to complete.

In my opinion, Microsoft took the right approach with GitHub Copilot. Although LLMs work surprisingly well for coding, they still make a ton of mistakes and don’t make sense as a stand-alone product. Copilot is a feature integrated into popular IDEs that pops up with suggestions when they are likely to help. You can review, edit, or improve on each suggestion.  

Again, don’t get me wrong. I think coding SOTA will drastically improve over the next few years, and one day, AI will be able to solve 80% of GitHub issues. Devin AI is still far away from that day, although the company has a valuation of $2 billion in 2024.

More formally, the framework for evaluation is:

  1. Find a relevant benchmark for a specific AI use case. 
  2. Find the current state-of-the-art (SOTA) error rate and human error rate on that benchmark.
  3. Is the SOTA better or comparable to the human error rate?
    1. If yes (unlikely): Great, the problem is solved, and you can create a stand-alone AI product by reproducing SOTA results.
    2. If no (likely): Check if there is a niche customer segment that is more tolerant of errors. If yes, you can still have a niche stand-alone product. If you can’t find such a niche, go to the next step.
  4. You can’t release a stand-alone AI product. Wait for SOTA to get better, pour money into research, or go to the next step.
  5. Think about how to integrate AI as a feature into the existing product. Make it easy for users to detect and correct AI’s mistakes. Then, measure AI’s return on investment:

    AI_ROI = Effort_saved_by_AI / Effort_lost_correcting_AI

    If too much user time is spent checking and correcting AI errors (AI_ROI<=1), you don’t even have a feature.

Or, to summarize everything discussed here in one sentence:

Every innovative AI use case will eventually become a feature or a product, once the error rates allow it. If you want to make it happen faster, become a researcher. OpenAI’s early employees spent seven years on AI research before overnight success with ChatGPT. Ilya Sutskever, OpenAI’s chief scientist, still didn’t want to release ChatGPT 3.5 because he was afraid it hallucinated too much. Science takes time.

If you found this article useful, please share.

 

 

Working Backwards Book Summary

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Why read the Working Backwards book?

Working-backwards-book-small.jpg

Amazon is not very popular right now, with many media criticizing its treatment of workers and its cut-throat culture. Still, love them or hate them, there are some undeniable achievements. Amazon is a tech giant with a market cap of over 1.4 trillion USD. For the first eight years, they operated with a loss as they reinvested all profits into expansion. To me, the most impressive thing about Amazon is that they succeeded in many different business fields. Other tech giants often have a primary business domain. E.g., Microsoft makes software, Apple premium consumer devices, and Facebook social media platforms.

But Amazon was revolutionary in all these fields:

  • E-commerce: First online bookseller and one of the first online stores.
  • E-commerce platform: Fulfillment by Amazon handles logistics for other sellers.
  • Logistics: Amazon Delivery and Amazon Prime (with same-day delivery).
  • Ebooks: Amazon Kindle, the most popular e-ink reader, and Kindle Library, the biggest ebook library.
  • Cloud computing: Amazon Web Services (AWS), the first and largest cloud vendor.
  • Voice assistants: Amazon Echo, the first voice assistant device.
  • Video streaming: Amazon Prime Video and Amazon Studios.
  • Consumer products: Amazon Fire tablet, Amazon Fire TV, and Amazon private labels
  • Retail: Whole Foods Market (acquired) and Amazon Go, the first cashierless convenience store.

Notice that the above domains require completely different know-how and different business principles. E-commerce is a low-margin business, logistics involves a lot of low-skilled employees, and cloud computing requires a small number of high-tech employees. Amazon Studios is an entirely different creative business, with connections to Hollywood and high margins. Amazon Kindle, Echo, and Go required the invention of new technologies. How can a single company excel at all of that?

The book’s thesis is that Amazon excels in different fields because of its unique “Amazonian” culture. 

Amazonian culture

Where does “Amazonian” culture come from? It is never directly stated in the book, but insider stories make it apparent that Jeff Bezos decides on Amazon culture. Jeff’s famous company-wide memos prohibit some practices and prescribe what to do instead. 

On the one hand, that is expected. Amazon, in 2022, had 1.6 million employees. Without strong management at the top, Amazon’s culture would be a mish-mash of cultures new employees would bring. On the other hand, a strong man at the top goes against common advice from conferences and management books: empowering your employees, having a flat organization, and deciding on company culture together with employees. I found similarities between Jeff Bezos and Steve Jobs — you are free to do whatever you want in a company that is not Amazon. While Steve Jobs insisted on product excellence, Jeff Bezos insisted on following Amazon’s procedures and principles.

Here are the central tenets of the Church of Jeff Bezos, in no particular order.

Decoupled two-pizza teams

Again, this principle goes against common wisdom. Many consultants and books say “communication is key” and “communicate important things to all relevant people.” In practice, this turns into large group meetings and emails with a dozen recipients, so employees spend most of their time in meetings or reading emails.

That is because the number of communication channels grows exponentially with the number of participants (a variant of Metcalfe’s law):

communication-channels-C_ScrumAlliance_org.png

An example from the book is a minor change in the Amazon Affiliates program. At first, Amazon gave affiliate commissions only for directly linked products. They decided to expand that to all products purchased by affiliate visitors. It was a straightforward change, and they estimated it could be released in a month. However, they first needed approval from the database team to make changes to the central Amazon database. Then they needed to get the approval and materials from the marketing team. Then they needed to get approval from the legal team. Then they need to write support documents and instruct support agents. All relevant parties need to be synchronized on the release date. In the end, a change a few people could implement in a week took six months!

Jeff recognized these internal dependencies are why companies get slower as they grow. He proposed a radical solution: structure Amazon around independent “two-pizza” teams (below ten people). If the team can’t be fed with two pizzas, it should be broken down. Each team has the resources and authority to release a feature without waiting for other teams. A team should expose their feature as an internal API and document how to use it. In the above example, the affiliate team should not depend on the database team to release a new version. There should be separate affiliate API and database API, with separate release schedules and documentation. If one team needs a meeting or written approval from another team to release a feature, that is a failure of an organization. 

But what if a change from one team breaks something or causes a problem? That can happen, but Jeff says breaking changes are not a problem if the change is reversible. Most tech problems can quickly be reverted, e.g., a problematic new feature can be disabled. Company coordination between many teams is needed only when the change is not easily reversible, e.g., shipping faulty devices to customers.

This is not how most companies operate and it is hard to implement. Bosses like to retain political control over many people and don’t like independent teams doing stuff without their knowledge. Developers don’t like writing internal documentation or exposing API when it takes less time to connect to a central database. And why write internal documentation when a coworker can ask you over the phone, email, or in a meeting? 

At Amazon, you need to do it because Jeff said so. And Jeff did such a good job breaking Amazon’s monolith into independent teams that he realized Amazon could expose internal APIs and documentation as public cloud services. That is how Amazon, then an e-commerce company, became a cloud computing vendor. In Q4 2021, Amazon Web Services generated 13% of Amazon’s revenue but 100% of Amazon’s profits (other divisions reinvested all profits). 

Notice that the “two-pizza teams” principle is similar to the “move fast and break things” idea. In both cases, it is more important to move fast, even with occasional problems, because most problems can quickly be fixed.

Six-page memos

In Jeff’s words, “Six-page memos are the smartest thing we ever did.“

In the beginning, Amazon used the same meeting structure as other companies:

  • A meeting organizer would prepare PowerPoint slides. 
  • While slides were presented, meeting participants could interrupt and ask questions.
  • A meeting would end with a discussion and decision on future actions. 

By 2004, Jeff started to hate this structure. On a plane, he had read a 25-page paper, The Cognitive Style of PowerPoint, which explained that bullet points are a terrible way to structure arguments. That paper proposed banning PowerPoint in organizations and using written text instead. The paper had such an influence that Jeff immediately sent an email banning PowerPoint and mandating narrative memos instead. In Jeff’s own words, “writing a good 4-page memo is harder than ‘writing’ a 20-page PowerPoint because the narrative structure of a good memo forces better thought and better understanding” and “​​If someone builds a list of bullet points in Word, that would be just as bad as PowerPoint.” In other words, bullet points are lazy thinking. 

That directive was met with strong resistance at Amazon. PowerPoint was standard, and making slides was easier than writing narrative memos. Memo needs to stand on its own, as there is no presenter who can explain bullets or who you can ask for clarification. For many managers, this seemed like an overly academic approach. But Jeff insisted.

From then on, meeting organizers would prepare a six-page memo they would not distribute in advance. Instead, all participants would get and read the memo in the first 20 minutes of a meeting in complete silence. This solves a few problems:

  • Preparation: With silent reading at the beginning, all participants are on the same page. Before, people who prepared or read meeting documents in advance had an advantage in the discussion.

  • Text limit: Because reading is limited to 20 minutes, a memo can’t be longer than six pages. Tricks to cram more text, like using smaller fonts or margins, make no sense with a 20-minute limit.

  • Shareability: A memo is standalone and can be shared with people outside the meeting. That doesn’t work with slides, as they depend on a presenter explaining them.

After reading (Jeff would often finish reading the last), participants would discuss and decide on the next steps.

It took some time for six-page memos to become part of Amazon’s culture. When it did, memo writing became competitive. The best writer on the team would produce a memo, and other team members would criticize it. The book mentions that successful six-page memos were distributed around the company as examples, and writing well became a vital skill at Amazon.

Working backwards

Once two-pizza teams are formed, and the new project is defined in a six-page memo, what is the next step? A usual corporate product development process would start with implementing the product, then creating marketing materials, and ending with a press release and support documents. 

Jeff Bezos noticed problems with that order:

  • It is company-centric to start with development first. In the development phase, a company is inclined to cut corners and reuse existing solutions that are not optimal for customers.

  • Project goals are not clear to all employees involved. Even developers are not always clear on which features are necessary and which are optional.

  • The product team often develops features that the marketing team doesn’t find marketable, and lacks features that would be great for marketing.

  • If the price is left unspecified, the product team will often create a product that is too expensive.

Instead, the process should be customer-centric and start with customer needs. Jeff asked teams to work backwards, starting from a target customer. The first thing a team needs to create is a press release for the fictitious product. Industry practice is that press releases should be 300-400 words, not longer. Since that limits a product description, only key features can be listed. A press release also explains use cases, target customers, price, and availability. After approval by management, the entire team reads it and knows what they are working on. The next thing developed after a press release is an internal FAQ (frequently asked questions) that explains all things team members need to know but are not in the press release, for example: which services, resources, or hardware is required, people involved, minor features, architecture, timeline, budget, edge cases, etc. The purpose of the FAQ is that all team members understand the details and commit to it. After the FAQ is approved, product development starts.

An example given in the book: when Kindle 2 was being developed, Jeff insisted that the press release includes “Whispersync.” Whispersync is an ability to wirelessly sync books, bookmarks, and reading progress over a GSM network. Previously, customers needed a cable, a PC, a sync application, and an internet connection to move books to Kindle. With Whispersync, purchased books would magically appear on your Kindle after a purchase. And the fictitious press release stated Whispersync will be free with every Kindle. Jeff and the marketing team loved it. The internal FAQ explained that Whispersync will use Whispernet, Amazon’s custom always-on 3G connection. That put a great burden on the product team. They needed to add a 3G modem, negotiate prices with network carriers to have an affordable 3G plan, incorporate the cost of 3G in the price of Kindle and books, and develop new syncing software. If the press release didn’t insist on that feature, employees would be inclined to do what is easiest for them: keep the existing cables and sync software. The fictitious press release made solving that customer pain point obligatory. When Kindle 2 was released, journalists were delighted to discuss the revolutionary Whispersync feature.  

Measure input metrics, not output metrics

Companies often focus on what Jeff calls “output metrics”: revenue, profits, stock price, market share, etc. They are “output” because they result from many input metrics, some of which we don’t control. For example, revenue depends on the current economy and seasonality, stock price depends on bear and bull markets, and market share depends on competitors. It is silly to be proud of increased monthly revenue if it was caused by holiday season shopping. It is silly to be proud of the increased stock price if lower interest rates drive it, not your actions. 

Jeff says that the primary metrics should be “input metrics” that you have direct control over. In the case of Amazon, input metrics are inventory size, prices, delivery times and prices, etc. If you have a large inventory, low prices, and fast and affordable delivery, then the output metrics like revenue will be good. It is the management responsibility to decide which input metrics are the right ones.

Jeff believes the right product metrics make the growth flywheel spin faster. Amazon’s e-commerce growth flywheel is below:

Amazon-flywheel-C_foundit_substack.png

Metrics that Amazon has control over (and are good input metrics) are product selection, prices, and customer experience (which includes delivery experience, website experience, refund experience, etc.).

However, it is more complex than given in the above diagram. The book provides an example of how in the early days, Amazon’s input metric was the “number of product detail pages.” However, then they noticed that an increase in the number of listed products didn’t cause an increase in revenue. It turned out that the inventory team added many products that were not popular, were out of stock, or had a long shipping time. They changed the input metric to “number of product page views X percentage of items in stock that can be shipped immediately,” which accounts for both product popularity and inventory. But, the book advised against having too complicated or too many input metrics. One department had many complex input metrics, resulting in employees not understanding how to influence them. They switched to simpler metrics, so everybody could understand how to contribute to the bottom line.

Single-threaded leadership

Companies always have a shortage of good managers to lead new projects. As a result, managers often need to manage multiple projects. That is bad. The best way to make a project fail is to give it to someone “30% of their time.” If the project is important, it deserves complete focus from a manager and a team. The book gives examples of times when Jeff moved heads of highly successful divisions to work on new things that made no revenue for years. Some managers thought they were being demoted by working on unprofitable projects, only to achieve wild success after a few years.

In Amazon’s terminology, single-threaded leadership is having a single-threaded owner heading a single-threaded team; both focused on the new project alone. Without dedicated focus, employees would revert to doing legacy work, as legacy work is bringing money.

Bar raiser for hiring

At the beginning, Amazon had a high hiring bar because Jeff Bezos had very high expectations. As the company grew, they noticed that the quality of new hires varied widely. That was mostly caused by the urgency bias — a candidate who was a poor fit still got hired because there was an urgency to fill their position. A new employee often didn’t match the expectations and left after a short period, returning the company to square one.

Even without urgency bias, new employees’ quality varied depending on who interviewed them and which process they used. For example, Jeff preferred candidates who excelled at academia, even if that was not necessary for their work. Other interviewers had lower criteria, did unstructured interviews, or asked questions that didn’t predict future performance. If one interviewer was against the hire, they were still inclined to approve it if other interviewers were eager to hire.

To improve hiring, Amazon found and trained internal “bar raisers.” A bar raiser is an employee outside a team who comes as an objective 3rd party to check if a new hire satisfies minimum hiring standards. They checked the procedure was followed, structured interviews were done, and that each interviewer voted “hire” or “no hire” in writing. Each interviewer needed to write their opinion before final approval, without knowing what other interviewers thought. 

Conclusion 

I really enjoyed reading this book. No matter what I think of Jeff Bezos, I like his ideas on how to organize a large corporation to behave like a startup. If you share the same sentiment, get the book in Kindle, audiobook, or paper format (I don’t earn a commission):

Working Backwards book on Amazon

 

 

Free Interior Design With Sweet Home 3D and SketchUp

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If you need a do-it-yourself interior design for your flat, house, basement, or dictator mansion—with completely free software—you are at the right place. Below is my experience with choosing software, finding 3D furniture models, finding textures, rendering, and furnishing the flat according to the interior design project. I used Sweet Home 3D, SketchUp, and SketchUp 3D Warehouse to produce results you can see in the gallery below:

Living room (from balcony)

Living room (from sofa)

Bedroom (from closet)

My friends were impressed with the fidelity of the initial interior design and the final result (“Hey, even the artwork is the same!). Let’s be clear, this is not professional interior design, but that was not the goal. Professionals buy interior design software for hundreds of dollars, design is their full-time job, and they charge you appropriately. 

Do-it-yourself interior design is less ambitious, less costly, and has two simple goals:

  1. Save time and reduce stress when buying furniture and picking colors because you will know exactly what furniture and colors you need.

  2. Make a better interior design than you would by just picking furniture and colors on the go. 

Even if you spend just 10 hours on planning your interior, that will save more time when shopping (“Is this the right size or color?”), and you will end up with a better interior you will live in. With that said, let’s dive in.

Picking software: why not commercial?

I first thought of buying professional interior design software but quickly got discouraged. Even SketchUp Go is $119 and SketchUp Studio is $699 per year. That is a bargain compared to AutoCAD, which is $235 per month. Even more problematic than price, professional software is overwhelming for new users. I didn’t want to spend weeks learning 3D modeling or lighting setup; I wanted to drag & drop 3D furniture on the floor plan and immediately see how it looks and fits the space. 

A less complex option is using web-based interior design tools for amateurs: FloorPlanner, Planner 5D, HomeByMe, RoomStyler, etc. They all have a simple interface, free trial, and inexpensive pricing. But I found these web-based tools quite limited:

  • A small number of 3D models.
  • No importing of free 3D models found on the internet.
  • No exporting of your finished plans.
  • Anything advanced is charged extra: hi-res renders, additional furniture, etc.
  • Your data is locked in their cloud forever.
  • If you stop paying a subscription, you will lose access to your projects.

Commercial software feels like Hotel California lyrics—”you can never leave”—either because you invested too much into learning it or your project files are all locked in the cloud.

Free software to the rescue

Fortunately, you can do interior design with entirely free software, which is interoperable with each other. You will need three parts: 

  • Interior design software.
  • Furniture libraries.
  • 3D modeling software for custom furniture.

Free interior design software: Sweet Home 3D (SH3D)

Sweet Home 3D (website, Wikipedia) is a free architectural design software. You can download the desktop version or use the online web version. 

SH3D advantages are:

  • Simple to use.
  • Workflow optimized for interior design.
  • Downloadable packages for extra 3D models and extra textures.
  • Importing 3D models and textures. 
  • Rendering of images and walkthrough videos.
  • Exporting project in OBJ format (e.g., for use in Blender).
  • Completely free.

For learning SH3D, I highly recommend YouTube SH3D lessons from TJ FREE. Included 14 lessons are short so you can watch them all in two hours.

Below is a short overview of the design process.

1. Create rooms and walls

To speed up the process, SH3D has the option to import a floor plan as a background image:

Then you create rooms and walls by drawing over the background image of a floor plan.

2. Place furniture and objects

Once you have a floor plan, it is time to add large objects like furniture. While adding furniture, SH3D will force you to think about furniture dimensions, which is good. You can position and rotate furniture to your liking until you find an organization that best works for you. 

SH3D has limited furniture options, but you can import more; see the 3D Warehouse or SketchUp sections below.

3. Add colors and textures

When furniture is in place, it is time to add some flair. Add colors or textures to walls, floors, tiles, or furniture. Note that you can import any texture from an image file. For example, I downloaded wallpaper textures and canvas photos from shopping websites to add exact wallpaper textures and exact images to canvas pieces hanging on the walls.

4. Create viewpoints for rendering

Once everything is set up, it is time to switch from the default view, which is not of great quality, to renders. I bet you will render the same views more than a dozen times (as your design progresses), so it is convenient to create viewpoints. Viewpoints are specific points in the floor plan where you want cameras to render from. Viewpoints can be defined from room corners, entrances, sitting areas, or any area that best showcases your creation. 

You can navigate to a viewpoint by its name and then request render from that viewpoint. However, note that rendering takes time; the better the quality, the longer rendering will take. 

5. Render viewpoints in a batch

The good thing with defined viewpoints is that you can render all defined viewpoints in a batch. Instead of selecting viewpoint by viewpoint and waiting for each render to finish, just run a batch renderer! Set quality to maximum, go for lunch, and by the time you finish your avocado toast, all viewpoints will be rendered.

Free furniture library: 3D Warehouse

Although SH3D extra libraries have thousands of 3D models, they lack specific furniture.

However, 3D Warehouse from SketchUp has tens of thousands more. After registration, models can be downloaded as COLLADA files (.dae file extension) that can be opened in SH3D or SketchUp. If you plan to furnish your interior with IKEA furniture, you are lucky, as more than 1000 IKEA models are available for free.

Free 3D modeling software: SketchUp Make 2017

What if a desired furniture piece is unavailable in SH3D, 3D Warehouse or anywhere online? No problem; you can create a new piece in SketchUp or start from a 3D model of a similar piece and modify it.

Unfortunately, SketchUp moved to paid plans in 2017. Fortunately, you can still download the last free version of SketchUp Make 2017 from 3rd party sites (Windows download, Mac download). That 2017 version will try to scare you by saying you will be “vulnerable to security issues.” Honestly, I think that is a silly sales tactic (aka FUD tactic), as no known malware can infect COLLADA files. Just click on the very hidden “I agree” button in the bottom right corner:

In SketchUp Make 2017, you can start from scratch or import an existing COLLADA file using File > Import… option:

You can learn SketchUp by watching one of many YouTube tutorials. There are even tutorials on how to build an entire house in SketchUp.

I found it faster and easier to do interior design in SH3D, so I used it only to modify existing furniture. But if you invest in learning SketchUp, you will be able to design anything, from products to models for 3D printing, not only interiors.

Conclusion

I showed how you can do basic interior design with free software. It is not professional work, but it will enable you to notice most of the problems with your ideas before you start buying furniture. You will see if the furniture is too big or too small for a room, if all doors can open or they bump into something, if there are too many or too few colors in the space. You will be able to show renders to your friends and family and get immediate feedback.

Furthermore, the above process is fast and easy even for people without 3D modeling or rendering experience.

You can download my final SH3D file with all furniture and textures inside so that you can play with it:

Both that file and this tutorial are licensed under a Creative Commons BY-NC-ND License. If you find this resource helpful, please share.



Daddy, did you really need to buy an electric car?

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I made a mistake: I bought an electric car. EV articles I have read on Hacker News and Reddit didn’t prepare me for a dozen EV infrastructure problems in my part of the EU. Anecdotes below explain lessons I learned the hard way.

When I see a cool new gadget, my rationality goes away. I tell myself, “Don’t buy another device that will somehow be discharged when you need it,” but to no avail. The siren call would sing, “It has USB-C!” until my wallet opens wider than the Spielberg’s “Jaws”.

This time, the siren call was a government incentive of 10,600 EUR ($12,100) for a new electric vehicle purchase. “Great,” I thought, “I need to replace my old car.” My experience of driving electric car-shares in Oxford and Berlin was great. So, I purchased a 1.5-ton gadget, with no USB-C ports included, called Hyundai Kona EV:

Why not a Tesla Model 3, you ask? Fun fact: Nikola Tesla was a naming inspiration for both Tesla cars and Nikola trucks. Tesla was an ethnic Serbian born in present-day Croatia, but you can’t officially buy a Tesla or Nikola vehicle in Croatia (where I live) or Serbia. Elon Musk tweeted his hope to open sales in the region early in 2020, but as of August 2020 that didn’t happen.

First, the good parts: as soon as I picked up my car in Zagreb, Croatia, I was impressed. EVs are so quiet that EU law requires they produce a buzzing sound when going slower than 30 km/h. Acceleration is instant; torque is strong enough, even in Eco mode, to make tires scream if I pushed the “gas” pedal too fast. There are no vibrations and no jerking when the car shifts gears. EVs use regenerative breaking, again completely silent. EV charging is currently free in Croatia, and depending on rainfall, 44% of that electricity is carbon-free. I was in love with the car.

Lessons 1-6, Slovenia

The first weekend I drove with a group of friends to a Toastmaster’s competition in Ljubljana, Slovenia, 143 km from Zagreb (83 mi). Kona EV with 64 kWh battery has a declared range of 450 km (280 mi), so even with 90% battery, I had enough for a round trip. So I thought. I soon learned the first lesson:

#1: EVs have 20% shorter range in cold weather.

It was November. But still, 450 x 90% x 0.8 = 324 km, round trip is 286 km, we were good. While driving 130 km/h on a highway (80mph, a legal limit), I noticed my range dropping significantly. The car specs sheet lied in the worst possible way: they were technically correct. High torque: true. Range of 450 km: true. But:

#2: EVs can’t deliver both performance and declared range at the same time, you need to choose one.

I had the wrong intuition that cars have a longer range on the highway than in the city. That is true for gas cars because gas cars are extremely inefficient in cities. All cylinders are running, swallowing gas when accelerating, and that kinetic energy is lost when braking. As EVs use only the energy needed and recuperate that energy when breaking, they really have a declared range when driving in the city. When driving fast, EV range shortens because air resistance is proportional to the square of speed. Gas engines are more efficient when all cylinders are firing at constant, high RPMs. As a result, gas cars have a maximum range at speeds of 89-97 km/h (55-60 mph). EVs have a maximum range at around 55 km/h (35 mph), and range falls linearly as you drive faster. For example, this is the range decrease for Tesla Model S, depending on speed and temperature:

As I slowed down, the estimated range increased. Unfortunately, part of the highway was closed for construction, and we needed to take a detour. The round trip just got longer, so I decided to park at the EV charging place. But, it was a Type 2 AC charger, meaning another lesson was coming:

#3: To charge at Type 2 AC plugs in the EU, you need to bring your cable.

Of course, I had none. Fast chargers I used before had attached cables, and I was wondering what was the point of the cable lock button on Hyundai Kona? It is there because:

#4: Lock your 230 EUR Type 2 cable if you don’t want it stolen.

Between event sessions, I was Googling “fast EV chargers in Slovenia.” Highway stops by “Petrol” had them, great. Around 11 PM, we left Ljubljana in high spirits. We arrived at the Petrol station and found a fast charger, with cables. Guess what happened next:

#5: EU fast chargers are activated via a proprietary chip card or smartphone app. Each company in each country uses a different card / app.

Of course, I only had a smartphone charging app for Croatia. Lessons were coming fast:

#5A: Petrol gas stations clerks can’t sell you a Petrol charging card, and they can’t activate an EV charging even when you want to pay for it.

“Download and register in the Petrol app,” the night clerk said. Yes, but:

#5B: Petrol Android app is 50 MB, so if a station is covered by a slow EDGE signal, you can’t download the app.

We got back to the highway, where my friend caught H+ signal and downloaded the app. At the next Petrol station, we discovered:

#5C: When you hit the “Start charging” button in the Petrol app, it redirects you to a registration form to enter payment and address.

No problem, I can fill the form, even on EDGE signal. But then:

#5D: To register in the Petrol app, needed for charging, you need a valid address in Slovenia.

It is good the EV charging station was 20 meters away from the gas station because I was cursing Petrol so loud a clerk would have had all rights to call the police. I should have read Petrol app comments beforehand:

It was past midnight when we got back on the highway. To extend the range, I slowed down to 75 km/h (46 mph). The limit was 130km/h, so a trailer truck started taking over us. Then I remembered a Tour de France strategy I saw on TV:

#6: In an emergency, you can extend the range of your EV by tailgating a  trailer truck, a strategy called aerodynamic drafting.

Don’t hold me responsible for traffic tickets or beating by an angry trucker. There I was, hidden behind a trailer truck, to save electricity while driving 90 km/h. The car showed it was using less power, and the range extended. When we entered Zagreb, there was 19 km left:

I went to a fast-charging station I could actually activate, and I fell asleep in the car.

Lessons 7-9, Croatia

I made fun of Petrol for their unfriendliness towards tourists, but at least the EV chargers at Petrol stations are working. In Croatia, you can’t always count on that. In my modest opinion, that stems from the fact that charging is free. Why is free electricity a problem, you ask? There is no commercial incentive to build and maintain charging stations. Instead, most chargers are built when the EU donates money or when the government is pressed to improve EV infrastructure. Local politicians come when a charging station is finished to deliver speeches: “This is an example of Croatia using EU funds wisely, for a green and sustainable future.” After the journalists leave, there is no economic interest in sustaining chargers in the future.

For example, this city-provided Type 2 charger has been broken for three months:

In front of the Zagreb city hall, one charging station displays an error:

While other just states it is out of order:

What I learned is:

#7: Free chargers are often unmaintained, and you are better with a commercial provider you can hold accountable.

“But, there is nothing to break on a charging station!” I thought. Boy, I was wrong. EV charging stations use internal computer for authorization, and for controlling the display and the charging protocol. Before charging starts, a car locks the cable with the mechanical pin and signals that back to the charger. When a car is unlocked, it signals a charger to drop the voltage to zero, so the mechanical pin holding cable can be released. Guess what?

#8: Some chargers are not compatible with some EVs and refuse to stop charging, resulting in the “Hotel California effect.” You can check out in the app, but you can never leave.

For example, Croatian Telecom chargers. The first time their cable got stuck in my car I panicked. Their app was not working, the charger was not responding to car signals, and their charging stations don’t have the stop button:

They have the contact phone, but it was Sunday afternoon. I called them, expecting three levels of menus and “we are not working on weekends” message. To my great surprise, a female voice answered after two rings. “I have a big problem with your EV charging station: I can’t get my car out because…” My explanation was stopped by “What is the serial number written on the station?” As I dictated the serial, I could hear fast typing. A few seconds later charging station stopped buzzing, display went blank for a moment, and I could unplug my car. “Wow, that was fast, thank you very much!” I said. Although they currently don’t charge for electricity, Croatian Telekom is a commercial operation. They have a direct 24/7 support line where a representative can reset any charging station in Croatia, provided the serial number. It seems that such a solution was easier than fixing charging stations or the charging app. I now just call them and say, “Can you reset the station with serial X?” If you work there, let me know, I would like to buy a box of chocolates for the team.

Unfortunately, EV charges are not the only thing broken here. Our driving culture is also broken:

#9: It is common to find a gas car parked at the EV charging spot, the practice called ICEing (internal combustion engine-ing).

One time I urgently needed to charge, and this was the charging spot:

ICEing is common around the world. Tesla implemented parking locks in China. German police are lifting cars instead of giving parking tickets. Croatian EV owners decided to stage a short protest, where they blocked access to a gas station with EVs, for five minutes:

Online reactions were not sympathetic:

I find Paul’s “they’re blocking poor people” comment particularly insightful. EV owners are currently perceived as rich geeks. There was a similar dilemma in the early 1900s: why would tax-payers money be used to make asphalted roads when only rich people can afford cars?

Lesson 10, Austria

After the New Year, my 10-year-old daughter and me were going for a skiing vacation to Austria. This time I prepared like it was D-day. For obvious reasons, I decided to skip Slovenia and charge somewhere in Austria. First charging option was a fast charger next to IKEA Graz, but that required registration with a SMATRICS, a setup fee, and a monthly subscription. The second option was a charger at our ski village, but I found out that to be a tourist trap, as they charged 230.40 EUR for 8 hours. In other words, they were selling electricity at 20x times the Austrian rate. My third option was a valid one: IONITY 50kWh chargers on a highway near Graz, no registration needed, and a full charge of 6.5 EUR. We stopped there, activated charging via an app, and went to a nearby restaurant for Schnitzel. By the time we finished a coffee, the car was fully charged. I could easily go to the wrong charging station, so the lesson was clear:

#10: Before a long EV trip, research all charging options, download necessary apps, and read negative user reviews to see if other people had a problem with charging.

While leaving Graz, I remembered that Tesla studied, but never finished, nearby Graz Polytechnic. The minor problem was that he came into conflict with a professor over the Gramme dynamo, when Tesla suggested that commutators were not necessary. The bigger problem was that he spent his nights gambling and got into gambling debt.

Lesson 11, Croatia

It was a May trip to Velebit mountain that finally convinced me buying an EV was a mistake.

My daughter, me, and a dozen other people were going for a weekend of camping. The camping site confirmed I could use one of electric sockets for motor homes. Just in case, I found two charging stations in a nearby town. Perfect.

As we arrived at the camping site, I started charging. Hyundai portable wall charger pulls a maximum of 2.8 kW, so I planned two nights of charging. I joined a barbecue and grabbed a beer. An hour later, the camp owner was searching for me.

“You plugged your EV, right?” he asked.

“Yes, it is charging fine,” I replied.

“Not anymore. The camp’s main electric fuse went off. We don’t have any electricity,” he explained. “Can you unplug it?”

He was apologetic. “Sorry for the situation, can you plug it again after 10 PM when everybody goes to sleep?”

As told, I plugged my car at 10:30 PM. Twenty minutes later, I saw the camp owner walking around with a lamp and asking people if they knew where the EV owner was.

“The main fuse again?” I asked.

“Yes,” he replied, “sorry, but you will have to charge your car somewhere else.”

That was a small private camp, and it seemed the owner had just extended electric cabling from the fuse box of his house. Electricity was used by his house, laundry room, a dozen plugs across the camp, and an electric heater for a glamping tent.

The next day, after hiking during the day, I drove 30 minutes to the Type 2 charger in Gospić. Unfortunately:

#11: AC Type 2 charger speed depends not on declared power (22 kW), but on the power of AC converter you have in your car. That makes Type 2 chargers useless for travel.

Out of AC charger declared for 22 kW, Tesla 3 can draw 11 kW, and Kona EV can draw 7.2 kW. I needed to leave my car for seven more hours, so I asked a friend to pick me up. When we got back to the camp, the joke was on me and my fancy electric car. The joke continued the following day because we needed to check out of the camp, without a car. My daughter went in one car, me to another, and our bags were stuffed in a third car. Then our gas car convoy took a detour to Gospić to drop us all off at the charging station.

As my daughter finally sat down in our car, she asked, “Daddy, did you really need to buy an electric car?”

There is a funny twist to the story. There was a charger closer to our camping site in a Nikola Tesla museum. Because, of all places, Tesla was born in the nearby village of Smiljan. I didn’t try that charger, as online comments explained it is located behind the museum fence. And the fence is closed when the museum is closed, as it was that weekend:

Lesson 12, Conclusion

Humans rationalize their mistakes, and I am not different. Buying an EV was a mistake, but I convinced myself it is not so bad. During six months, I have wasted 10+ hours on the above EV charging issues. But that is still less than 54 hours annually Americans spend stalled in traffic or 44 hours annually Brits spend searching for parking. EV charging is a hassle, but it is only a problem when going on a longer trip. In city, you deal with rush hours and parking problems every day. The final lesson I learned:

#12: Today’s cars are great, but the car infrastructure sucks. With an EV, in addition to traffic jams and parking, you will regularly have to deal with charger infrastructure.

Nikola Tesla understood that the biggest obstacle to electricity adoption was infrastructure. He invented AC that is easier to transform to high voltages and transport over large distances. 

After winning the War of the currents, Tesla aimed even higher. He wanted to use currents with specific frequencies for wireless power transfer via earth or air. Early radio receivers used this idea and had large antennas that would provide both signal and electric power wirelessly. But Tesla dreamed bigger, envisioning high-frequency transmitting towers powering electric airplanes:

Instead, we got a world where it is difficult to charge an EV even when standing next to an electrical plug. But this time dreams failed because of people, not because of technology.

 

UPDATE: check discussions on Hacker News, r/TrueReddit, and r/ElectricVehicles.

Screwed by Lufthansa and the German Government, Saved by PayPal

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The travel and tourism sectors are suffering because of the COVID-19 pandemic, and governments are scrambling to offer subsidies to affected businesses. But, there is little talk on how companies are transferring their costs to citizens, with government approval.

In this case, the company is Lufthansa, who canceled my May flight from Croatia to Germany three weeks before the flight date. I booked the flight before the pandemic started, so I was a bit relieved. My reason for being in Berlin was valid for entry, but I was to either self-isolate for two weeks or get tested for COVID-19, both a hassle. If Lufthansa decided to cancel the flight and return the money, no problem.

But that is not what Lufthansa decided. Their cancellation email didn’t mention refunds, but offered flight vouchers instead. As the email was a no-reply email, calling customer support was the only option. Lufthansa’s support representative agreed to issue a full refund. But they said that processing the refund can take up to six weeks, as they are overwhelmed with requests. EU consumer protection law requires refunds for an undelivered product or service to be issued within 30 days, but these are exceptional times, so I agreed. What Lufthansa didn’t agree to was stating that in writing, in a letter or email.

Six weeks came and passed without a refund. On a second call, Lufthansa’s support representative repeated the story. They are overwhelmed with requests, my refund will be processed in 2-3 weeks, no need to worry, but they will not provide that statement in writing. 

While waiting for their promise, I stumbled across an article explaining my issue as part of a bigger, EU-wide story. Lufthansa was on the verge of bankruptcy and agreed to a €9 billion bailout from the German government. In an effort to save the company, the German government told Lufthansa that they don’t need to obey EU consumer protection law, and that they don’t need to issue refunds. Lufthansa can issue vouchers for future travels instead. This situation is controversial because:

  • The German government is telling a German company they don’t need to follow EU law.
  • The German government is playing favorites with one company. Other companies don’t have such luxury during the crisis, both in getting the loan or escaping the laws.
  • German tax-payers are giving a large, risky loan to one air carrier.
  • With the voucher system, other EU citizens are effectively giving indefinite, interest-free loans to Lufthansa.

Notice that the procedure of getting a voucher from Lufthansa is much easier than getting a refund. For a voucher, click on the link in the email and fill a form. For a refund, wait on the customer support line. But I am a stubborn person, and I hate vouchers. A few times in my life, given vouchers got unused or companies put restrictions on voucher use. In this case, there is a possibility Lufthansa will go bankrupt, and then their vouchers will be as useful as toilet paper. Wait, that may come in handy in COVID-19 times!

I was not surprised when three weeks passed and there was no refund. I felt screwed by Lufthansa, the EU, and German politicians. However, there was still one overseas ace up my sleeve I could use. 

I paid for my flight via PayPal, which offers consumer protection on purchases, and I decided to activate it. I didn’t have much hope, as worldwide pandemic cancellations were not typical PayPal disputes. Additionally, I didn’t have much proof except for the cancellation email. Lufthansa didn’t provide a written reply, the flight was erased from the Lufthansa website, and I didn’t record phone conversations. 

But, as soon I made my claim, I realized there is a hidden benefit. When a PayPal claim is created, there is a deadline and a written trail. In this case, Lufthansa was given until July 13th to respond:

It seems that someone from Lufthansa replied before that, as I got my money on July 7th:

PayPal-case-history

I am impressed by PayPal’s straightforward claims procedure. There was no paper forms or PDFs that I needed to sign. As an example, my friend Chris also got a Lufthansa April transatlantic flight canceled (a month before my trip). He asked Lufthansa for a refund, and to this day he still hasn’t received any of 700 EUR.

You have all the facts above, so make your conclusions. These are my modest takeaways:

  • During a crisis, EU laws get overridden by national interests.
  • During a crisis, businesses labeled a “national interest” by politicians get favorable treatment.
  • PayPal consumer protection works, even in times of crisis.
  • Creating a PayPal or credit company claim is easier than waiting on customer support lines, enforces deadlines, and has traceable communication.

To come to the beginning of this article, there are analyses of money lost by different business sectors due to COVID-19. It would be interesting to see an analysis of how much consumer money is currently locked in unused vouchers, and what percentage of them will actually get used in the future. For comparison, in normal times, just US consumers lose up to $3 billion annually in unspent gift cards. It seems that 2020 is going to be an outlier.

 

UPDATE: check the discussions on Hacker News and Reddit.

Breaking the Ice for Underwater Hockey

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“This is for you kid,” said my uncle, “have fun!”

I was 12 and I got my first mask and fins. I loved it. I would go every day to the seaside, to the rocky pier, and dive next to rusty, oily boats. I would take a deep breath and plunge to depths eager to see what is below. And there were so many things: broken plates, coca-cola cans, and even some fish. I got better and better until I could freedive 14 meters deep to the old car tire hosting a crab who was surprised by a pimply visitor.

“This must be how being a dolphin feels like!” I thought to myself. They are air-breathing mammals, same as me, going periodically to surface to take a breath. Then they would dive under and play catch with each other.

28 years later I was killing my time drinking beer in a smoky Berlin bar. “..underwater hockey,” mentioned a girl next to me. I turned my head and joined the enthusiastic conversation about the obscure underwater sport. “If you did free diving, you should totally do it, it is great!” Sumi said. Playing underwater like dolphins? I was in.

When I came for Wednesday training, the entire team assembled to help me. I got pink fins from one person, a white hockey stick from another, and mask from the third. Alex took it as his job to train me before the practice match. We did puck pushing, passing, and turning in a small kids pool. Easy-peasy-lemon-squeezy. I couldn’t wait to get into the big pool and score some goals. It was going to be great.

We entered the big pool and divided into two teams. I was assigned to be front right, as passing partner to Alex. “Start!”, someone shouted and we all swam towards the little green puck. As I dived in, Alex was already two meters in front of me. He didn’t have the puck for long, two people from another team attacked and engaged in something like underwater tennis. I got myself into action, meaning I got somebody’s fin in my butt, and somebody’s else knee in my face. I mean, I am not sure knee and fin were not from the same person, we were all a big ball of human flesh. In the excitement of that sexy moment, I forgot that air is a necessary requirements for a long life. My brain suddenly reminded me with a gentle thought “Air! Get some air or you will die!” and adrenaline overdose. I jumped out of the water like a mating salmon and started hyperventilating. A girl dived out next to me, took three breaths, gave me a look and dived down again. “Boy, these people are hard-core,” I said to myself and dived down. When I looked around, the puck was already on a different part of the pool. I mean, I am not sure if the puck was there, but there is where I saw an amorphous ball of hands, panties, and fins so I swam in that direction. As I approached, a battle of underwater tenis was abruptly interrupted by a white-pants enemy player who decided to play for himself and separated from the crowd. I swam to block his way, but it was like chasing a torpedo. He whizzed by me and put the puck in a metal goal.

Our brave team regrouped and started talking about strategy for the next round. My strategy was not to forget to breathe. At a start signal, we rushed again to the center, and this time I was watching for my chance from the surface. When crowd around the puck got smaller I dived in and actually got to touch the puck with my stick. My joy was not long-lived, as a member of the other team started fighting for it with me. In that tug-of-war, I was pushing as hard as I could, but the opponent had the same cunning plan. My brain screamed “Air!” and I went out. By the time my heart stopped pounding, the puck was again in our goal. Seems both my team’s strategy and my personal strategy to remember to breathe were falling. In addition, a team member told me that it is not allowed to push the stick with two hands. I guess the underwater police didn’t care because I lost the puck anyway.

In the third round, I didn’t get to any action, but at least I got some air.

Then, our team got a “penalty shot”. My team member would start in the back and I would be in the front. We assembled at the bottom of the pool and started. The puck was suddenly next to me, finally my chance! I took it, swam by the opposing player and went unstoppably towards their goal. Wow, can’t believe it, I am going to score. Then I noticed everybody stayed in the first half of the pool. I swam to the surface and Alex told me “You are not supposed to take the puck away when there is a penalty kick, we were just supposed to protect the sides pg the penalty kicker.” Andy Warhol once said, “In the future, everyone will be famous for 15 minutes.” It seems my future was not going to happen that day, and I will have to swallow much more chlorinated water before I become good with these underwater battles.

The game continued at the same pace for 20 more minutes. A lot of feeding frenzy scenes with human bodies all over each other like Berghain dark room at 4 am. Diving down, fighting for a puck, remembering to get air. Rinse, repeat. I got a better feeling for a strategy of the game, which is not surprising considering where I started. One time I even succeeded to intercept the torpedo with white pants, yay!

After half an hour the game ended. I thanked everybody for lending me their equipment and went for a long hot-shower meditation. My heart was still fast and chlorinated water was running from my nose. As I arrived home I dropped tiredly to my bed. With a smile on my face. Maybe my underwater play was more that of a sea cow than a dolphin, but still, it was a great, great experience that I want to repeat.

 

Computers Have Had Emotions for Quite Some Time

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A common assumption is that computers can’t have emotions. But there is a strong philosophical argument that AI systems have had emotions for many decades now.

Before making an argument, we need to define “emotion”. That definition shouldn’t require consciousness self-awareness (reddit was fast to correct this) or physical manifestation.

Self-awareness can’t be a requirement for the presence of emotions because that would contradict current research findings that even simple animals have emotions. Experiments on honeybees in 2011 show that agitated honeybees display an increased expectation of bad outcomes, similar to the emotional state displayed by vertebrates. Research published in Science in 2014 concluded that crayfish show anxiety-like behavior controlled by serotonin. However, we wouldn’t consider honeybees or crayfish to be self-aware. But you don’t have to look to the animal world. When you are sleeping, you are not self-aware, yet when a bad nightmare wakes you up, would you say you didn’t experience emotions?

Physical manifestation in any form (facial expression, gesture, voice, sweating, heart rate, etc.), can’t be a requirement for the presence of emotions because it would imply that people with complete paralysis (e.g. Stephen Hawking) don’t experience emotions. And, as before, we have the sleep problem: you experience emotions in your dreams, even when your body doesn’t show it.

This is a bit of a problem. As self-awareness is not a requirement, we can’t simply ask the subject if they experience emotions. As a physical manifestation is not a requirement, we can’t simply observe the subject. So, how do we determine if one is capable of emotional response?

As a starting point, let’s look at evolution:

The evolutionary purpose of emotions in animals and humans is to direct behavior toward specific, simple, innate needs: food, sex, shelter, teamwork, raising offspring, etc.

Emotional subsystems in living creatures do that by constantly analyzing their current model of the world. Generally wanted behavior produces positive emotions (happiness, love, etc.) while generally unwanted behavior produces negative emotions (fear, sadness, etc.).

Emotions are simple and sometimes irrational, so evolution enabled intelligence to partially suppress emotions. When we sense that lovely smell of freshly baked goods, we feel a craving to eat them, but we can suppress the urge because we know they are not healthy for us.

Based on that, we can provide a more general definition of “emotion” for any intelligent agent:

Emotion is an output of an irrational, built-in, fast subsystem that constantly evaluates the agent’s world model and directs the agent’s focus toward desired behavior.

Take a look at a classic diagram of a model-based, utility-based agent (from Artificial Intelligence: A Modern Approach textbook), and you will find something similar:

Do you notice it? In the middle of the diagram stands this funny little artifact:

Even professional philosophers in the realm of AI have overlooked this. Many presume AI systems are rational problem solvers that calculate an optimal plan for achieving a goal. Utility-based agents are nothing like that. Utility function is always simple, ignores a lot of model details, and is often wrong. It is an irrational component of the system.

But why would anybody put such a silly thing in code? Because introducing “happiness” to an AI system solves the computational explosion problem. The real world, and even many mathematical problems, has many more possible outcomes than particles in the universe. A nonoptimal solution is better than no solution at all. And paradoxically, utility-based agents make more efficient use of computational resources, so they produce better solutions.

To understand this, let’s examine two famous AI systems from the 1990s that used utility functions to play a simple game.

The first one is Deep Blue, a computer specifically designed to crunch chess data. It was a big black box with 30 processors and 480 special-purpose chess chips, and it was capable of evaluating 200 million chess positions per second. But even that is not enough to play perfect chess, as the shannon number states that the lower bound of possible situations in a chess game is 10120. To overcome this, engineers could have limited search to only N future chess moves. But there was a better approach: Deep Blue could plan longer into the future if it could discard unpromising combinations.

Human chess players had known for a long time an incorrect but fast way to do that. Count the number of chess pieces on the board and multiply by the value of each piece. Most chess books say that your pawn is worth one point and the queen is worth nine points. Deep Blue had such a utility function, which enabled it to go many moves deeper. With the help of this utility function, Deep Blue defeated Garry Kasparov in 1997.

It is important to note two things:

  1. A utility function is irrational. Kids play chess by counting numbers of pieces; grandmasters do not. In the chess game of the century, 13-year-old Bobby Fischer defeated a top chess master by sacrificing the queen. He was executing a strategy, not counting pieces.
  2. A utility function needs to be irrational. If it were rational, it would calculate every possible move, which would make it slow and therefore defeat its purpose. Instead, it needs to be simple and very fast, so it can be calculating in every nanosecond.

This chess experiment proved that utility-based agents that use “intuition” to achieve solutions vastly outperform perfectly rational AI systems.

But it gets even better.

At the same time that IBM was pouring money in Deep Blue, two programmers started developing a downloadable chess program you could run on any PC. Deep Fritz ran on retail hardware, so it was able to analyze only 8 million positions per second—so it was 25 times slower than Deep Blue. But the developers realized they could beat the game with a better utility function. After all, that is how humans play: they are slower but have stronger intuition.

In 1995 the Deep Blue prototype lost to Deep Fritz, which was running on a 90MhZ Pentium. How is it possible that the 25-times-slower computer won? It had better utility function that made the program “happy” with better moves. Or should we say it had better “emotional intelligence”?

This shows the power of emotion. The immediacy of the real world requires that you sometimes stop thinking and just go with your gut feeling, programmed into you by billions of years of evolution. Not only is there a conflict between emotions and rationality, but different emotions also play tug-of-war with each other. For example, a hungry animal will overcome its fear and take risks to get food.

Note that in both higher-order animals and advanced AI systems, the fixed part of a utility function is augmented with utility calculation based on experience. For example, a fixed part of human taste perception is a love of sugars and a strong dislike for rotten eggs. But if one gets sick after eating a bowl of gummy bears, the association “gummy bears cause sickness” is stored and retrieved in the future, as a disgusting taste. The author of this article is painfully aware of that association, after a particular gummy bear incident from his childhood.

To summarize the main points:

  • Emotions are fast subsystems that evaluate the agent’s current model of the world and constantly provide positive or negative feedback, directing action.
  • Because emotional subsystems need to provide immediate feedback, they need to be computationally fast. As a consequence, they are irrational.
  • Emotions are still rational on a statistical level, as they condense “knowledge” that has worked many times in the past.
  • In the case of animals, utility functions are crafted by evolution. In the case of AI agents, they are crafted by us. In both cases, a utility function can rapidly look up past experience to guide actions.
  • Real-world agents don’t have only one emotion but a myriad of them, the interplay of which directs agents into satisfying solutions.

In conclusion, an AI agent is emotional if it has a utility function that (a) is separate from the main computational part that contains the world model and (b) constantly monitors its world model and provides positive or negative feedback.

Utility-based agents that play chess satisfy those criteria, so I consider them emotional—although in a very primitive way.

Obviously, this is not the same as human emotions, which are much more intricate. But the principle is the same. The fact that honeybees and crayfish have very simple emotional subsystems doesn’t change the fact that they experience emotions. And if we consider honeybees and crayfish emotional, then we should do the same with complex utility-based agents.

This may feel implausible. But we need to ask ourselves, is that because the above arguments are wrong? Or, maybe, because the utility function in our brain is a little out of date?

 

 

Zeljko Svedic is a Berlin-based tech philosopher. If you liked this piece of modern philosophy, you will probably like Singularity and the Anthropocentric Bias and Car Sharing and the Death of Parking.

Wanted: Collaborative Writer in Berlin

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“The advantage of collaborative writing is that you end up with something for which you will not be personally blamed.”—Scott Adams

This is a unique job, for unique writers. The client is a well-off individual, the owner of a boring software company. To compensate for that, he writes long, in-depth articles for his blog, Vice Motherboard or scripts for his YouTube channel. The problem is that he writes slowly, has little time, and has another 50+ ideas for unfinished articles. This is where you come in.

Your job will be to meet the client in Prenzlauer Berg, Berlin, and collaboratively work on new writing projects. The client will provide you with an idea, the reasoning behind an article, and an outline of a text. Your creative neurons will then do the magic of converting the rough idea into a popular article that will be loved and shared by geeks worldwide. This is not ghostwriting; you are going to be co-author on the piece. The salary starts from 260 EUR per thousand words.

Sounds interesting? However, there are some requirements you need to fulfill:

  • You need to be a better writer than the client. “Better” is a subjective term, but the number of readers and shares is not. Be prepared to show your best work and their impact.
  • You need to be on the geeky/science/philosophy side. If you noticed, all the articles above are non-fiction, and deep into geek culture.
  • You need to be funnier than the client. That is not going to be hard.
  • Native or near-native English writing skills.

And to recap, the benefits are:

  • 20 hours per week (half-time position).
  • Location in Prenzlauer Berg, Berlin.
  • Working on a variety of interesting tech and science topics.
  • Competitive salary, starting from 260 EUR per thousand words.

Are you ready to change the world with your writing? Apply here.

 

MasterCard Serbia asked ladies to share FB photos of, among other things, their credit card

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Credit card companies should know all about phishing, right? McCann should know all about marketing, right? Combine the two in Serbia and you will get a marketing campaign that just went viral, although for the wrong reasons.

Mastercard Serbia organised a prize contest “Always with you” that asks female customers to share contents of their purse on Facebook. If you read the text carefully, it is not required to photo your card. However, the example photo clearly shows the credit card details of a fictive customer:

Lured by prizes, many customers posted photos of their private stuff. And some copied Mastercard promo — their credit card, with full details, is visible in the photo:

This is the first phishing campaign that I know that was organised by credit company itself!

The funny thing that is that nobody in Mastercard, McCann agency or legal team noticed the problem. There is a lengthy legal document explaining the conditions of the prize contest:

That document is signed by Mastercard Europe SA and McCann Ltd Belgrade, so it seems it has passed multiple levels of corporate approval. And Mastercard didn’t seem to notice the problem until six days later when a serbian security blogger wrote about it.

In my modest opinion, the lesson of this story is to be careful how you hire. I am biased because I run an employee assessment company, but smiling people with lovely résumés can still be bozos. And when you have incompetent people in the company, it doesn’t matter what formal company procedures you have in place.

 

P.S. As user edent from HN noticed, photo sharing of credit cards is nothing uncommon for Twitter: https://twitter.com/needadebitcard

P.P.S. As of today (May 18), entire “Always with you” campaign is deleted from Facebook.