More on Entrepreneurship/Creators

Navdeep Yadav
2 years ago
31 startup company models (with examples)
Many people find the internet's various business models bewildering.
This article summarizes 31 startup e-books.
1. Using the freemium business model (free plus premium),
The freemium business model offers basic software, games, or services for free and charges for enhancements.
Examples include Slack, iCloud, and Google Drive
Provide a rudimentary, free version of your product or service to users.
Google Drive and Dropbox offer 15GB and 2GB of free space but charge for more.
Freemium business model details (Click here)
2. The Business Model of Subscription
Subscription business models sell a product or service for recurring monthly or yearly revenue.
Examples: Tinder, Netflix, Shopify, etc
It's the next step to Freemium if a customer wants to pay monthly for premium features.
Subscription Business Model (Click here)
3. A market-based business strategy
It's an e-commerce site or app where third-party sellers sell products or services.
Examples are Amazon and Fiverr.
On Amazon's marketplace, a third-party vendor sells a product.
Freelancers on Fiverr offer specialized skills like graphic design.
Marketplace's business concept is explained.
4. Business plans using aggregates
In the aggregator business model, the service is branded.
Uber, Airbnb, and other examples
Marketplace and Aggregator business models differ.
Amazon and Fiverr link merchants and customers and take a 10-20% revenue split.
Uber and Airbnb-style aggregator Join these businesses and provide their products.
5. The pay-as-you-go concept of business
This is a consumption-based pricing system. Cloud companies use it.
Example: Amazon Web Service and Google Cloud Platform (GCP) (AWS)
AWS, an Amazon subsidiary, offers over 200 pay-as-you-go cloud services.
“In short, the more you use the more you pay”
When it's difficult to divide clients into pricing levels, pay-as-you is employed.
6. The business model known as fee-for-service (FFS)
FFS charges fixed and variable fees for each successful payment.
For instance, PayU, Paypal, and Stripe
Stripe charges 2.9% + 30 per payment.
These firms offer a payment gateway to take consumer payments and deposit them to a business account.
Fintech business model
7. EdTech business strategy
In edtech, you generate money by selling material or teaching as a service.
edtech business models
Freemium When course content is free but certification isn't, e.g. Coursera
FREE TRIAL SkillShare offers free trials followed by monthly or annual subscriptions.
Self-serving marketplace approach where you pick what to learn.
Ad-revenue model The company makes money by showing adverts to its huge user base.
Lock-in business strategy
Lock in prevents customers from switching to a competitor's brand or offering.
It uses switching costs or effort to transmit (soft lock-in), improved brand experience, or incentives.
Apple, SAP, and other examples
Apple offers an iPhone and then locks you in with extra hardware (Watch, Airpod) and platform services (Apple Store, Apple Music, cloud, etc.).
9. Business Model for API Licensing
APIs let third-party apps communicate with your service.
Uber and Airbnb use Google Maps APIs for app navigation.
Examples are Google Map APIs (Map), Sendgrid (Email), and Twilio (SMS).
Business models for APIs
Free: The simplest API-driven business model that enables unrestricted API access for app developers. Google Translate and Facebook are two examples.
Developer Pays: Under this arrangement, service providers such as AWS, Twilio, Github, Stripe, and others must be paid by application developers.
The developer receives payment: These are the compensated content producers or developers who distribute the APIs utilizing their work. For example, Amazon affiliate programs
10. Open-source enterprise
Open-source software can be inspected, modified, and improved by anybody.
For instance, use Firefox, Java, or Android.
Google paid Mozilla $435,702 million to be their primary search engine in 2018.
Open-source software profits in six ways.
Paid assistance The Project Manager can charge for customization because he is quite knowledgeable about the codebase.
A full database solution is available as a Software as a Service (MongoDB Atlas), but there is a fee for the monitoring tool.
Open-core design R studio is a better GUI substitute for open-source applications.
sponsors of GitHub Sponsorships benefit the developers in full.
demands for paid features Earn Money By Developing Open Source Add-Ons for Current Products
Open-source business model
11. The business model for data
If the software or algorithm collects client data to improve or monetize the system.
Open AI GPT3 gets smarter with use.
Foursquare allows users to exchange check-in locations.
Later, they compiled large datasets to enable retailers like Starbucks launch new outlets.
12. Business Model Using Blockchain
Blockchain is a distributed ledger technology that allows firms to deploy smart contracts without a central authority.
Examples include Alchemy, Solana, and Ethereum.
Business models using blockchain
Economy of tokens or utility When a business uses a token business model, it issues some kind of token as one of the ways to compensate token holders or miners. For instance, Solana and Ethereum
Bitcoin Cash P2P Business Model Peer-to-peer (P2P) blockchain technology permits direct communication between end users. as in IPFS
Enterprise Blockchain as a Service (Baas) BaaS focuses on offering ecosystem services similar to those offered by Amazon (AWS) and Microsoft (Azure) in the web 3 sector. Example: Ethereum Blockchain as a Service with Bitcoin (EBaaS).
Blockchain-Based Aggregators With AWS for blockchain, you can use that service by making an API call to your preferred blockchain. As an illustration, Alchemy offers nodes for many blockchains.
13. The free-enterprise model
In the freeterprise business model, free professional accounts are led into the funnel by the free product and later become B2B/enterprise accounts.
For instance, Slack and Zoom
Freeterprise companies flourish through collaboration.
Start with a free professional account to build an enterprise.
14. Business plan for razor blades
It's employed in hardware where one piece is sold at a loss and profits are made through refills or add-ons.
Gillet razor & blades, coffee machine & beans, HP printer & cartridge, etc.
Sony sells the Playstation console at a loss but makes up for it by selling games and charging for online services.
Advantages of the Razor-Razorblade Method
lowers the risk a customer will try a product. enables buyers to test the goods and services without having to pay a high initial investment.
The product's ongoing revenue stream has the potential to generate sales that much outweigh the original investments.
Razor blade business model
15. The business model of direct-to-consumer (D2C)
In D2C, the company sells directly to the end consumer through its website using a third-party logistic partner.
Examples include GymShark and Kylie Cosmetics.
D2C brands can only expand via websites, marketplaces (Amazon, eBay), etc.
D2C benefits
Lower reliance on middlemen = greater profitability
You now have access to more precise demographic and geographic customer data.
Additional space for product testing
Increased customisation throughout your entire product line-Inventory Less
16. Business model: White Label vs. Private Label
Private label/White label products are made by a contract or third-party manufacturer.
Most amazon electronics are made in china and white-labeled.
Amazon supplements and electronics.
Contract manufacturers handle everything after brands select product quantities on design labels.
17. The franchise model
The franchisee uses the franchisor's trademark, branding, and business strategy (company).
For instance, KFC, Domino's, etc.
Subway, Domino, Burger King, etc. use this business strategy.
Many people pick a franchise because opening a restaurant is risky.
18. Ad-based business model
Social media and search engine giants exploit search and interest data to deliver adverts.
Google, Meta, TikTok, and Snapchat are some examples.
Users don't pay for the service or product given, e.g. Google users don't pay for searches.
In exchange, they collected data and hyper-personalized adverts to maximize revenue.
19. Business plan for octopuses
Each business unit functions separately but is connected to the main body.
Instance: Oyo
OYO is Asia's Airbnb, operating hotels, co-working, co-living, and vacation houses.
20, Transactional business model, number
Sales to customers produce revenue.
E-commerce sites and online purchases employ SSL.
Goli is an ex-GymShark.
21. The peer-to-peer (P2P) business model
In P2P, two people buy and sell goods and services without a third party or platform.
Consider OLX.
22. P2P lending as a manner of operation
In P2P lending, one private individual (P2P Lender) lends/invests or borrows money from another (P2P Borrower).
Instance: Kabbage
Social lending lets people lend and borrow money directly from each other without an intermediary financial institution.
23. A business model for brokers
Brokerages charge a commission or fee for their services.
Examples include eBay, Coinbase, and Robinhood.
Brokerage businesses are common in Real estate, finance, and online and operate on this model.
Buy/sell similar models Examples include financial brokers, insurance brokers, and others who match purchase and sell transactions and charge a commission.
These brokers charge an advertiser a fee based on the date, place, size, or type of an advertisement. This is known as the classified-advertiser model. For instance, Craiglist
24. Drop shipping as an industry
Dropshipping allows stores to sell things without holding physical inventories.
When a customer orders, use a third-party supplier and logistic partners.
Retailer product portfolio and customer experience Fulfiller The consumer places the order.
Dropshipping advantages
Less money is needed (Low overhead-No Inventory or warehousing)
Simple to start (costs under $100)
flexible work environment
New product testing is simpler
25. Business Model for Space as a Service
It's centered on a shared economy that lets millennials live or work in communal areas without ownership or lease.
Consider WeWork and Airbnb.
WeWork helps businesses with real estate, legal compliance, maintenance, and repair.
26. The business model for third-party logistics (3PL)
In 3PL, a business outsources product delivery, warehousing, and fulfillment to an external logistics company.
Examples include Ship Bob, Amazon Fulfillment, and more.
3PL partners warehouse, fulfill, and return inbound and outbound items for a charge.
Inbound logistics involves bringing products from suppliers to your warehouse.
Outbound logistics refers to a company's production line, warehouse, and customer.
27. The last-mile delivery paradigm as a commercial strategy
Last-mile delivery is the collection of supply chain actions that reach the end client.
Examples include Rappi, Gojek, and Postmates.
Last-mile is tied to on-demand and has a nighttime peak.
28. The use of affiliate marketing
Affiliate marketing involves promoting other companies' products and charging commissions.
Examples include Hubspot, Amazon, and Skillshare.
Your favorite youtube channel probably uses these short amazon links to get 5% of sales.
Affiliate marketing's benefits
In exchange for a success fee or commission, it enables numerous independent marketers to promote on its behalf.
Ensure system transparency by giving the influencers a specific tracking link and an online dashboard to view their profits.
Learn about the newest bargains and have access to promotional materials.
29. The business model for virtual goods
This is an in-app purchase for an intangible product.
Examples include PubG, Roblox, Candy Crush, etc.
Consumables are like gaming cash that runs out. Non-consumable products provide a permanent advantage without repeated purchases.
30. Business Models for Cloud Kitchens
Ghost, Dark, Black Box, etc.
Delivery-only restaurant.
These restaurants don't provide dine-in, only delivery.
For instance, NextBite and Faasos
31. Crowdsourcing as a Business Model
Crowdsourcing = Using the crowd as a platform's source.
In crowdsourcing, you get support from people around the world without hiring them.
Crowdsourcing sites
Open-Source Software gives access to the software's source code so that developers can edit or enhance it. Examples include Firefox browsers and Linux operating systems.
Crowdfunding The oculus headgear would be an example of crowdfunding in essence, with no expectations.

Scrum Ventures
3 years ago
Trends from the Winter 2022 Demo Day at Y Combinators
Y Combinators Winter 2022 Demo Day continues the trend of more startups engaging in accelerator Demo Days. Our team evaluated almost 400 projects in Y Combinator's ninth year.
After Winter 2021 Demo Day, we noticed a hurry pushing shorter rounds, inflated valuations, and larger batches.
Despite the batch size, this event's behavior showed a return to normalcy. Our observations show that investors evaluate and fund businesses more carefully. Unlike previous years, more YC businesses gave investors with data rooms and thorough pitch decks in addition to valuation data before Demo Day.
Demo Day pitches were virtual and fast-paced, limiting unplanned meetings. Investors had more time and information to do their due research before meeting founders. Our staff has more time to study diverse areas and engage with interesting entrepreneurs and founders.
This was one of the most regionally diversified YC cohorts to date. This year's Winter Demo Day startups showed some interesting tendencies.
Trends and Industries to Watch Before Demo Day
Demo day events at any accelerator show how investment competition is influencing startups. As startups swiftly become scale-ups and big success stories in fintech, e-commerce, healthcare, and other competitive industries, entrepreneurs and early-stage investors feel pressure to scale quickly and turn a notion into actual innovation.
Too much eagerness can lead founders to focus on market growth and team experience instead of solid concepts, technical expertise, and market validation. Last year, YC Winter Demo Day funding cycles ended too quickly and valuations were unrealistically high.
Scrum Ventures observed a longer funding cycle this year compared to last year's Demo Day. While that seems promising, many factors could be contributing to change, including:
Market patterns are changing and the economy is becoming worse.
the industries that investors are thinking about.
Individual differences between each event batch and the particular businesses and entrepreneurs taking part
The Winter 2022 Batch's Trends
Each year, we also wish to examine trends among early-stage firms and YC event participants. More international startups than ever were anticipated to present at Demo Day.
Less than 50% of demo day startups were from the U.S. For the S21 batch, firms from outside the US were most likely in Latin America or Europe, however this year's batch saw a large surge in startups situated in Asia and Africa.
YC Startup Directory
163 out of 399 startups were B2B software and services companies. Financial, healthcare, and consumer startups were common.
Our team doesn't plan to attend every pitch or speak with every startup's founders or team members. Let's look at cleantech, Web3, and health and wellness startup trends.
Our Opinions Following Conversations with 87 Startups at Demo Day
In the lead-up to Demo Day, we spoke with 87 of the 125 startups going. Compared to B2C enterprises, B2B startups had higher average valuations. A few outliers with high valuations pushed B2B and B2C means above the YC-wide mean and median.
Many of these startups develop business and technology solutions we've previously covered. We've seen API, EdTech, creative platforms, and cybersecurity remain strong and increase each year.
While these persistent tendencies influenced the startups Scrum Ventures looked at and the founders we interacted with on Demo Day, new trends required more research and preparation. Let's examine cleantech, Web3, and health and wellness startups.
Hardware and software that is green
Cleantech enterprises demand varying amounts of funding for hardware and software. Although the same overarching trend is fueling the growth of firms in this category, each subgroup has its own strategy and technique for investigation and identifying successful investments.
Many cleantech startups we spoke to during the YC event are focused on helping industrial operations decrease or recycle carbon emissions.
Carbon Crusher: Creating carbon negative roads
Phase Biolabs: Turning carbon emissions into carbon negative products and carbon neutral e-fuels
Seabound: Capturing carbon dioxide emissions from ships
Fleetzero: Creating electric cargo ships
Impossible Mining: Sustainable seabed mining
Beyond Aero: Creating zero-emission private aircraft
Verdn: Helping businesses automatically embed environmental pledges for product and service offerings, boost customer engagement
AeonCharge: Allowing electric vehicle (EV) drivers to more easily locate and pay for EV charging stations
Phoenix Hydrogen: Offering a hydrogen marketplace and a connected hydrogen hub platform to connect supply and demand for hydrogen fuel and simplify hub planning and partner program expansion
Aklimate: Allowing businesses to measure and reduce their supply chain’s environmental impact
Pina Earth: Certifying and tracking the progress of businesses’ forestry projects
AirMyne: Developing machines that can reverse emissions by removing carbon dioxide from the air
Unravel Carbon: Software for enterprises to track and reduce their carbon emissions
Web3: NFTs, the metaverse, and cryptocurrency
Web3 technologies handle a wide range of business issues. This category includes companies employing blockchain technology to disrupt entertainment, finance, cybersecurity, and software development.
Many of these startups overlap with YC's FinTech trend. Despite this, B2C and B2B enterprises were evenly represented in Web3. We examined:
Stablegains: Offering consistent interest on cash balance from the decentralized finance (DeFi) market
LiquiFi: Simplifying token management with automated vesting contracts, tax reporting, and scheduling. For companies, investors, and finance & accounting
NFTScoring: An NFT trading platform
CypherD Wallet: A multichain wallet for crypto and NFTs with a non-custodial crypto debit card that instantly converts coins to USD
Remi Labs: Allowing businesses to more easily create NFT collections that serve as access to products, memberships, events, and more
Cashmere: A crypto wallet for Web3 startups to collaboratively manage funds
Chaingrep: An API that makes blockchain data human-readable and tokens searchable
Courtyard: A platform for securely storing physical assets and creating 3D representations as NFTs
Arda: “Banking as a Service for DeFi,” an API that FinTech companies can use to embed DeFi products into their platforms
earnJARVIS: A premium cryptocurrency management platform, allowing users to create long-term portfolios
Mysterious: Creating community-specific experiences for Web3 Discords
Winter: An embeddable widget that allows businesses to sell NFTs to users purchasing with a credit card or bank transaction
SimpleHash: An API for NFT data that provides compatibility across blockchains, standardized metadata, accurate transaction info, and simple integration
Lifecast: Tools that address motion sickness issues for 3D VR video
Gym Class: Virtual reality (VR) multiplayer basketball video game
WorldQL: An asset API that allows NFT creators to specify multiple in-game interpretations of their assets, increasing their value
Bonsai Desk: A software development kit (SDK) for 3D analytics
Campfire: Supporting virtual social experiences for remote teams
Unai: A virtual headset and Visual World experience
Vimmerse: Allowing creators to more easily create immersive 3D experiences
Fitness and health
Scrum Ventures encountered fewer health and wellness startup founders than Web3 and Cleantech. The types of challenges these organizations solve are still diverse. Several of these companies are part of a push toward customization in healthcare, an area of biotech set for growth for companies with strong portfolios and experienced leadership.
Here are several startups we considered:
Syrona Health: Personalized healthcare for women in the workplace
Anja Health: Personalized umbilical cord blood banking and stem cell preservation
Alfie: A weight loss program focused on men’s health that coordinates medical care, coaching, and “community-based competition” to help users lose an average of 15% body weight
Ankr Health: An artificial intelligence (AI)-enabled telehealth platform that provides personalized side effect education for cancer patients and data collection for their care teams
Koko — A personalized sleep program to improve at-home sleep analysis and training
Condition-specific telehealth platforms and programs:
Reviving Mind: Chronic care management covered by insurance and supporting holistic, community-oriented health care
Equipt Health: At-home delivery of prescription medical equipment to help manage chronic conditions like obstructive sleep apnea
LunaJoy: Holistic women’s healthcare management for mental health therapy, counseling, and medication
12 Startups from YC's Winter 2022 Demo Day to Watch
Bobidi: 10x faster AI model improvement
Artificial intelligence (AI) models have become a significant tool for firms to improve how well and rapidly they process data. Bobidi helps AI-reliant firms evaluate their models, boosting data insights in less time and reducing data analysis expenditures. The business has created a gamified community that offers a bug bounty for AI, incentivizing community members to test and find weaknesses in clients' AI models.
Magna: DeFi investment management and token vesting
Magna delivers rapid, secure token vesting so consumers may turn DeFi investments into primitives. Carta for Web3 allows enterprises to effortlessly distribute tokens to staff or investors. The Magna team hopes to allow corporations use locked tokens as collateral for loans, facilitate secondary liquidity so investors can sell shares on a public exchange, and power additional DeFi applications.
Perl Street: Funding for infrastructure
This Fintech firm intends to help hardware entrepreneurs get financing by [democratizing] structured finance, unleashing billions for sustainable infrastructure and next-generation hardware solutions. This network has helped hardware entrepreneurs achieve more than $140 million in finance, helping companies working on energy storage devices, EVs, and creating power infrastructure.
CypherD: Multichain cryptocurrency wallet
CypherD seeks to provide a multichain crypto wallet so general customers can explore Web3 products without knowledge hurdles. The startup's beta app lets consumers access crypto from EVM blockchains. The founders have crypto, financial, and startup experience.
Unravel Carbon: Enterprise carbon tracking and offsetting
Unravel Carbon's AI-powered decarbonization technology tracks companies' carbon emissions. Singapore-based startup focuses on Asia. The software can use any company's financial data to trace the supply chain and calculate carbon tracking, which is used to make regulatory disclosures and suggest carbon offsets.
LunaJoy: Precision mental health for women
LunaJoy helped women obtain mental health support throughout life. The platform combines data science to create a tailored experience, allowing women to access psychotherapy, medication management, genetic testing, and health coaching.
Posh: Automated EV battery recycling
Posh attempts to solve one of the EV industry's largest logistical difficulties. Millions of EV batteries will need to be decommissioned in the next decade, and their precious metals and residual capacity will go unused for some time. Posh offers automated, scalable lithium battery disassembly, making EV battery recycling more viable.
Unai: VR headset with 5x higher resolution
Unai stands apart from metaverse companies. Its VR headgear has five times the resolution of existing options and emphasizes human expression and interaction in a remote world. Maxim Perumal's method of latency reduction powers current VR headsets.
Palitronica: Physical infrastructure cybersecurity
Palitronica blends cutting-edge hardware and software to produce networked electronic systems that support crucial physical and supply chain infrastructure. The startup's objective is to build solutions that defend national security and key infrastructure from cybersecurity threats.
Reality Defender: Deepfake detection
Reality Defender alerts firms to bogus users and changed audio, video, and image files. Reality Deference's API and web app score material in real time to prevent fraud, improve content moderation, and detect deception.
Micro Meat: Infrastructure for the manufacture of cell-cultured meat
MicroMeat promotes sustainable meat production. The company has created technologies to scale up bioreactor-grown meat muscle tissue from animal cells. Their goal is to scale up cultured meat manufacturing so cultivated meat products can be brought to market feasibly and swiftly, boosting worldwide meat consumption.
Fleetzero: Electric cargo ships
This startup's battery technology will make cargo ships more sustainable and profitable. Fleetzero's electric cargo ships have five times larger profit margins than fossil fuel ships. Fleetzeros' founder has marine engineering, ship operations, and enterprise sales and business experience.

Nick Nolan
3 years ago
In five years, starting a business won't be hip.
People are slowly recognizing entrepreneurship's downside.
Growing up, entrepreneurship wasn't common. High school class of 2012 had no entrepreneurs.
Businesses were different.
They had staff and a lengthy history of achievement.
I never wanted a business. It felt unattainable. My friends didn't care.
Weird.
People desired degrees to attain good jobs at big companies.
When graduated high school:
9 out of 10 people attend college
Earn minimum wage (7%) working in a restaurant or retail establishment
Or join the military (3%)
Later, entrepreneurship became a thing.
2014-ish
I was in the military and most of my high school friends were in college, so I didn't hear anything.
Entrepreneurship soared in 2015, according to Google Trends.
Then more individuals were interested. Entrepreneurship went from unusual to cool.
In 2015, it was easier than ever to build a website, run Facebook advertisements, and achieve organic social media reach.
There were several online business tools.
You didn't need to spend years or money figuring it out. Most entry barriers were gone.
Everyone wanted a side gig to escape the 95.
Small company applications have increased during the previous 10 years.
2011-2014 trend continues.
2015 adds 150,000 applications. 2016 adds 200,000. Plus 300,000 in 2017.
The graph makes it look little, but that's a considerable annual spike with no indications of stopping.
By 2021, new business apps had doubled.
Entrepreneurship will return to its early 2010s level.
I think we'll go backward in 5 years.
Entrepreneurship is half as popular as it was in 2015.
In the late 2020s and 30s, entrepreneurship will again be obscure.
Entrepreneurship's decade-long splendor is fading. People will cease escaping 9-5 and launch fewer companies.
That’s not a bad thing.
I think people have a rose-colored vision of entrepreneurship. It's fashionable. People feel that they're missing out if they're not entrepreneurial.
Reality is showing up.
People say on social media, "I knew starting a business would be hard, but not this hard."
More negative posts on entrepreneurship:
Luke adds:
Is being an entrepreneur ‘healthy’? I don’t really think so. Many like Gary V, are not role models for a well-balanced life. Despite what feel-good LinkedIn tells you the odds are against you as an entrepreneur. You have to work your face off. It’s a tough but rewarding lifestyle. So maybe let’s stop glorifying it because it takes a lot of (bleepin) work to survive a pandemic, mental health battles, and a competitive market.
Entrepreneurship is no longer a pipe dream.
It’s hard.
I went full-time in March 2020. I was done by April 2021. I had a good-paying job with perks.
When that fell through (on my start date), I had to continue my entrepreneurial path. I needed money by May 1 to pay rent.
Entrepreneurship isn't as great as many think.
Entrepreneurship is a serious business.
If you have a 9-5, the grass isn't greener here. Most people aren't telling the whole story when they post on social media or quote successful entrepreneurs.
People prefer to communicate their victories than their defeats.
Is this a bad thing?
I don’t think so.
Over the previous decade, entrepreneurship went from impossible to the finest thing ever.
It peaked in 2020-21 and is returning to reality.
Startups aren't for everyone.
If you like your job, don't quit.
Entrepreneurship won't amaze people if you quit your job.
It's irrelevant.
You're doomed.
And you'll probably make less money.
If you hate your job, quit. Change jobs and bosses. Changing jobs could net you a greater pay or better perks.
When you go solo, your paycheck and perks vanish. Did I mention you'll fail, sleep less, and stress more?
Nobody will stop you from pursuing entrepreneurship. You'll face several challenges.
Possibly.
Entrepreneurship may be romanticized for years.
Based on what I see from entrepreneurs on social media and trends, entrepreneurship is challenging and few will succeed.
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Amelia Winger-Bearskin
3 years ago
Reasons Why AI-Generated Images Remind Me of Nightmares
AI images are like funhouse mirrors.
Google's AI Blog introduced the puppy-slug in the summer of 2015.
Puppy-slug isn't a single image or character. "Puppy-slug" refers to Google's DeepDream's unsettling psychedelia. This tool uses convolutional neural networks to train models to recognize dataset entities. If researchers feed the model millions of dog pictures, the network will learn to recognize a dog.
DeepDream used neural networks to analyze and classify image data as well as generate its own images. DeepDream's early examples were created by training a convolutional network on dog images and asking it to add "dog-ness" to other images. The models analyzed images to find dog-like pixels and modified surrounding pixels to highlight them.
Puppy-slugs and other DeepDream images are ugly. Even when they don't trigger my trypophobia, they give me vertigo when my mind tries to reconcile familiar features and forms in unnatural, physically impossible arrangements. I feel like I've been poisoned by a forbidden mushroom or a noxious toad. I'm a Lovecraft character going mad from extradimensional exposure. They're gross!
Is this really how AIs see the world? This is possibly an even more unsettling topic that DeepDream raises than the blatant abjection of the images.
When these photographs originally circulated online, many friends were startled and scandalized. People imagined a computer's imagination would be literal, accurate, and boring. We didn't expect vivid hallucinations and organic-looking formations.
DeepDream's images didn't really show the machines' imaginations, at least not in the way that scared some people. DeepDream displays data visualizations. DeepDream reveals the "black box" of convolutional network training.
Some of these images look scary because the models don't "know" anything, at least not in the way we do.
These images are the result of advanced algorithms and calculators that compare pixel values. They can spot and reproduce trends from training data, but can't interpret it. If so, they'd know dogs have two eyes and one face per head. If machines can think creatively, they're keeping it quiet.
You could be forgiven for thinking otherwise, given OpenAI's Dall-impressive E's results. From a technological perspective, it's incredible.
Arthur C. Clarke once said, "Any sufficiently advanced technology is indistinguishable from magic." Dall-magic E's requires a lot of math, computer science, processing power, and research. OpenAI did a great job, and we should applaud them.
Dall-E and similar tools match words and phrases to image data to train generative models. Matching text to images requires sorting and defining the images. Untold millions of low-wage data entry workers, content creators optimizing images for SEO, and anyone who has used a Captcha to access a website make these decisions. These people could live and die without receiving credit for their work, even though the project wouldn't exist without them.
This technique produces images that are less like paintings and more like mirrors that reflect our own beliefs and ideals back at us, albeit via a very complex prism. Due to the limitations and biases that these models portray, we must exercise caution when viewing these images.
The issue was succinctly articulated by artist Mimi Onuoha in her piece "On Algorithmic Violence":
As we continue to see the rise of algorithms being used for civic, social, and cultural decision-making, it becomes that much more important that we name the reality that we are seeing. Not because it is exceptional, but because it is ubiquitous. Not because it creates new inequities, but because it has the power to cloak and amplify existing ones. Not because it is on the horizon, but because it is already here.

Ari Joury, PhD
3 years ago
7 ways to turn into a major problem-solver
For some people, the glass is half empty. For others, it’s half full. And for some, the question is, How do I get this glass totally full again?
Problem-solvers are the last group. They're neutral. Pragmatists.
Problems surround them. They fix things instead of judging them. Problem-solvers improve the world wherever they go.
Some fail. Sometimes their good intentions have terrible results. Like when they try to help a grandma cross the road because she can't do it alone but discover she never wanted to.
Most programmers, software engineers, and data scientists solve problems. They use computer code to fix problems they see.
Coding is best done by understanding and solving the problem.
Despite your best intentions, building the wrong solution may have negative consequences. Helping an unwilling grandma cross the road.
How can you improve problem-solving?
1. Examine your presumptions.
Don’t think There’s a grandma, and she’s unable to cross the road. Therefore I must help her over the road. Instead think This grandma looks unable to cross the road. Let’s ask her whether she needs my help to cross it.
Maybe the grandma can’t cross the road alone, but maybe she can. You can’t tell for sure just by looking at her. It’s better to ask.
Maybe the grandma wants to cross the road. But maybe she doesn’t. It’s better to ask!
Building software is similar. Do only I find this website ugly? Who can I consult?
We all have biases, mental shortcuts, and worldviews. They simplify life.
Problem-solving requires questioning all assumptions. They might be wrong!
Think less. Ask more.
Secondly, fully comprehend the issue.
Grandma wants to cross the road? Does she want flowers from the shop across the street?
Understanding the problem advances us two steps. Instead of just watching people and their challenges, try to read their intentions.
Don't ask, How can I help grandma cross the road? Why would this grandma cross the road? What's her goal?
Understand what people want before proposing solutions.
3. Request more information. This is not a scam!
People think great problem solvers solve problems immediately. False!
Problem-solvers study problems. Understanding the problem makes solving it easy.
When you see a grandma struggling to cross the road, you want to grab her elbow and pull her over. However, a good problem solver would ask grandma what she wants. So:
Problem solver: Excuse me, ma’am? Do you wish to get over the road? Grandma: Yes indeed, young man! Thanks for asking. Problem solver: What do you want to do on the other side? Grandma: I want to buy a bouquet of flowers for my dear husband. He loves flowers! I wish the shop wasn’t across this busy road… Problem solver: Which flowers does your husband like best? Grandma: He loves red dahlia. I usually buy about 20 of them. They look so pretty in his vase at the window! Problem solver: I can get those dahlia for you quickly. Go sit on the bench over here while you’re waiting; I’ll be back in five minutes. Grandma: You would do that for me? What a generous young man you are!
A mediocre problem solver would have helped the grandma cross the road, but he might have forgotten that she needs to cross again. She must watch out for cars and protect her flowers on the way back.
A good problem solver realizes that grandma's husband wants 20 red dahlias and completes the task.
4- Rapid and intense brainstorming
Understanding a problem makes solutions easy. However, you may not have all the information needed to solve the problem.
Additionally, retrieving crucial information can be difficult.
You could start a blog. You don't know your readers' interests. You can't ask readers because you don't know who they are.
Brainstorming works here. Set a stopwatch (most smartphones have one) to ring after five minutes. In the remaining time, write down as many topics as possible.
No answer is wrong. Note everything.
Sort these topics later. Programming or data science? What might readers scroll past—are these your socks this morning?
Rank your ideas intuitively and logically. Write Medium stories using the top 35 ideas.
5 - Google it.
Doctor Google may answer this seemingly insignificant question. If you understand your problem, try googling or binging.
Someone has probably had your problem before. The problem-solver may have posted their solution online.
Use others' experiences. If you're social, ask a friend or coworker for help.
6 - Consider it later
Rest your brain.
Reread. Your brain needs rest to function.
Hustle culture encourages working 24/7. It doesn't take a neuroscientist to see that this is mental torture.
Leave an unsolvable problem. Visit friends, take a hot shower, or do whatever you enjoy outside of problem-solving.
Nap.
I get my best ideas in the morning after working on a problem. I couldn't have had these ideas last night.
Sleeping subconsciously. Leave it alone and you may be surprised by the genius it produces.
7 - Learn to live with frustration
There are problems that you’ll never solve.
Mathematicians are world-class problem-solvers. The brightest minds in history have failed to solve many mathematical problems.
A Gordian knot problem can frustrate you. You're smart!
Frustration-haters don't solve problems well. They choose simple problems to avoid frustration.
No. Great problem solvers want to solve a problem but know when to give up.
Frustration initially hurts. You adapt.
Famous last words
If you read this article, you probably solve problems. We've covered many ways to improve, so here's a summary:
Test your presumptions. Is the issue the same for everyone else when you see one? Or are your prejudices and self-judgments misguiding you?
Recognize the issue completely. On the surface, a problem may seem straightforward, but what's really going on? Try to see what the current situation might be building up to by thinking two steps ahead of the current situation.
Request more information. You are no longer a high school student. A two-sentence problem statement is not sufficient to provide a solution. Ask away if you need more details!
Think quickly and thoroughly. In a constrained amount of time, try to write down all your thoughts. All concepts are worthwhile! Later, you can order them.
Google it. There is a purpose for the internet. Use it.
Consider it later at night. A rested mind is more creative. It might seem counterintuitive to leave a problem unresolved. But while you're sleeping, your subconscious will handle the laborious tasks.
Accept annoyance as a normal part of life. Don't give up if you're feeling frustrated. It's a step in the procedure. It's also perfectly acceptable to give up on a problem because there are other, more pressing issues that need to be addressed.
You might feel stupid sometimes, but that just shows that you’re human. You care about the world and you want to make it better.
At the end of the day, that’s all there is to problem solving — making the world a little bit better.

Vitalik
3 years ago
An approximate introduction to how zk-SNARKs are possible (part 1)
You can make a proof for the statement "I know a secret number such that if you take the word ‘cow', add the number to the end, and SHA256 hash it 100 million times, the output starts with 0x57d00485aa". The verifier can verify the proof far more quickly than it would take for them to run 100 million hashes themselves, and the proof would also not reveal what the secret number is.
In the context of blockchains, this has 2 very powerful applications: Perhaps the most powerful cryptographic technology to come out of the last decade is general-purpose succinct zero knowledge proofs, usually called zk-SNARKs ("zero knowledge succinct arguments of knowledge"). A zk-SNARK allows you to generate a proof that some computation has some particular output, in such a way that the proof can be verified extremely quickly even if the underlying computation takes a very long time to run. The "ZK" part adds an additional feature: the proof can keep some of the inputs to the computation hidden.
You can make a proof for the statement "I know a secret number such that if you take the word ‘cow', add the number to the end, and SHA256 hash it 100 million times, the output starts with 0x57d00485aa". The verifier can verify the proof far more quickly than it would take for them to run 100 million hashes themselves, and the proof would also not reveal what the secret number is.
In the context of blockchains, this has two very powerful applications:
- Scalability: if a block takes a long time to verify, one person can verify it and generate a proof, and everyone else can just quickly verify the proof instead
- Privacy: you can prove that you have the right to transfer some asset (you received it, and you didn't already transfer it) without revealing the link to which asset you received. This ensures security without unduly leaking information about who is transacting with whom to the public.
But zk-SNARKs are quite complex; indeed, as recently as in 2014-17 they were still frequently called "moon math". The good news is that since then, the protocols have become simpler and our understanding of them has become much better. This post will try to explain how ZK-SNARKs work, in a way that should be understandable to someone with a medium level of understanding of mathematics.
Why ZK-SNARKs "should" be hard
Let us take the example that we started with: we have a number (we can encode "cow" followed by the secret input as an integer), we take the SHA256 hash of that number, then we do that again another 99,999,999 times, we get the output, and we check what its starting digits are. This is a huge computation.
A "succinct" proof is one where both the size of the proof and the time required to verify it grow much more slowly than the computation to be verified. If we want a "succinct" proof, we cannot require the verifier to do some work per round of hashing (because then the verification time would be proportional to the computation). Instead, the verifier must somehow check the whole computation without peeking into each individual piece of the computation.
One natural technique is random sampling: how about we just have the verifier peek into the computation in 500 different places, check that those parts are correct, and if all 500 checks pass then assume that the rest of the computation must with high probability be fine, too?
Such a procedure could even be turned into a non-interactive proof using the Fiat-Shamir heuristic: the prover computes a Merkle root of the computation, uses the Merkle root to pseudorandomly choose 500 indices, and provides the 500 corresponding Merkle branches of the data. The key idea is that the prover does not know which branches they will need to reveal until they have already "committed to" the data. If a malicious prover tries to fudge the data after learning which indices are going to be checked, that would change the Merkle root, which would result in a new set of random indices, which would require fudging the data again... trapping the malicious prover in an endless cycle.
But unfortunately there is a fatal flaw in naively applying random sampling to spot-check a computation in this way: computation is inherently fragile. If a malicious prover flips one bit somewhere in the middle of a computation, they can make it give a completely different result, and a random sampling verifier would almost never find out.
It only takes one deliberately inserted error, that a random check would almost never catch, to make a computation give a completely incorrect result.
If tasked with the problem of coming up with a zk-SNARK protocol, many people would make their way to this point and then get stuck and give up. How can a verifier possibly check every single piece of the computation, without looking at each piece of the computation individually? There is a clever solution.
see part 2
