The Problem: Bridge protocols don’t enable users to bridge between Layer 2s
Crypto holders should be able to use their tokens whenever they want, across any set of blockchain networks.
Ethereum gets you security, but it is a very expensive layer. The benefit of Layer 2 networks is that they are more scalable – fees are lower in L2.
Unfortunately, users cannot move funds directly between Layer 2 networks.
Interoperability for tokens among different chains in web3 is a critical technical problem with crypto bridges today.
Bridging between Layer 2 networks is impossible
For example, users can’t go directly from from Optimism to Arbitrum, no matter what bridge they use.
In order to move tokens between these layer 2 networks, users are forced to take a multi-step process, bridging back to Layer 1 from Optimism before moving their funds to Arbitrum.
You can try it to see for yourself.
As shown below, after adding both Arbitrum Network AND Optimism Network to my wallet and going to the Arbitrum Bridge, its clear that the only connection possible is to go between Layer 1 and Layer 2.
Layer 2 direct jumps don’t exist (yet).
Current Functionality: Bridging from Ethereum to Layer 2 is simple
Bridging from Ethereum Mainnet any one of the Layer 2’s is pretty simple and straightforward.
For example, a user’s first experience exploring the Optimism ecosystem (covered in this post) is pretty straightforward:
The Ethereum –> Optimism bridge allows for funds to be transferred quickly, letting the user get started with dapps like NFTs, DeFi, and more.
Similarly, bridging from Ethereum Mainnet to Arbitrum is pretty easy.
Users use a service like Chainlist to connect to any number of different Layer 2 blockchain networks directly, without manually typing in the network ID and other information.
Optimism and Arbitrum are just two examples – Chainlist offers hundreds of connectable networks – an overwhelmingly large amount of software to explore.
Improving crypto bridges will reduce fees for users
The fact that a user can’t move funds between two different rollups without sending funds back to Ethereum Mainnet not only makes the user experience more cumbersome, but it means users pay higher fees.
The bridging process requires two additional transactions on Ethereum Mainnet, instead of a single transaction on Layer 2 between networks.
This multi-step process ultimately requires more gas.
At this point, the ability to move funds between layer 2 networks is an aspect of UX that is missing, and is a big gap in crypto.
The ideal UX features simply have not been built out yet.
Poor user experience is the nature of emerging technology
When you are exploring the frontier of emerging technology, some difficulty of use should be expected.
To put things into perspective, this lack of user functionality is part of what makes crypto most exciting.
If the UX was perfect and every blockchain app was easy to use, then crypto would already be mainstream.
When you are using emerging technology before other people, it is going to be clunky and difficult to use.
We are early; blockchain technology still has so much un-realized value. This is part of exploring and seeing tech trends before everyone else.
We Need to Enable Users to Migrate Between Layer 2 Blockchains
The good news, is that interoperability problem currently exists within similarly EVM compatible blockchains.
Because Layer 2 networks share protocols with Ethereum’s foundational Layer 1, the challenge should be solvable.
The Ethereum ecosystem, with the EVM protocol fundamentals, was made for this type of universal compatibility… shared components allowing protocols to integrate.
Most people don’t understand blockchain, let alone Ethereum.
What is Blockchain?
Blockchain is basically a database that everyone shares.
Anyone can write to the database.
Blockchain enables users to attain self sovereignty over their money and wealth. To do so, all you need is a non-custodial crypto wallet.
What is Ethereum?
Ethereum is a decentralized world computer.
Ethereum possesses all the key tenets of decentralization, security, and cryptography, which are fundamental to blockchain.
Beyond that, Ethereum is fully programmable, where any application can be built.
The investment case for Ethereum (and hence ETH) is that it will become the most liquid token in a digital economy built atop of a Turing-complete decentralized computer that can execute smart contracts.
How do most people view Ethereum, and what are they missing?
Ethereum means many things to different people.
To some, it is a cryptocurrency… a token and you can buy and sell and speculate on, just like any stock or asset.
To other people, Ethereum is the entry point into the world of DeFi, and the slightly shady world of lending, borrowing and yield farming.
Some people use Ethereum buy into web3 projects like NFT’s or other crypto tokens.
To others, Ethereum is like a cousin of Bitcoin with high-transaction fees.
All of these various “identities” that Ethereum might take on express the magic of the Ethereum Virtual Machine.
When you combine all these applications of Ethereum, you start to see the big picture:
Ethereum is more than a cryptocurrency – it is programmable money.
Of course, the first use case of blockchain was the Bitcoin cryptocurrency, and it is extremely valuable because it solves the double spend problem.
But the principles of blockchain that enable currency can be applied in many other unique and creative ways. This is what Ethereum focuses on.
Solving the double spend problem is just the first of many.
However, in order to apply blockchain tech to problems in the world and on the internet, we needed a way to build apps and systems that has blockchain technology built in.
We needed a blockchain software development platform.
This is why Ethereum was invented.
Ethereum is a Platform Where Any Application Can Be Built
Ethereum is first and foremost a computer built on the technical fundamentals of blockchain.
Remember computers in the 90s and 2000s and even the ones we have now aka smartphones?
Well, the Ethereum computer is like that but its built on a new architecture using blockchain.
The technical fundamentals of Ethereum are sound. Its a distributed computer, meaning it runs on a network of linked nodes instead of a single motherboard and processor.
And blockchains fundamentals of decentralization, cryptography, and security have been built in… so its different from other computers because of this.
Given that Ethereum is a computer, this means that Ethereum can serve as a platform that allows applications to flourish. Ethereum it provides the tools for this.
Solidity is the Programming Language for Ethereum
By tools, I’m talking programming languages, developer documentation, Github repositories, communities, etc.
Ethereum actually has a programming language called “Solidity” that allows any developers to write code and build an application using the Ethereum blockchain as a platform.
Doing so allows developers to build the front-end that users interact with, while maintaining the solid technical fundamentals like decentralization that Ethereum promises.
Since 2017 when I first learned about Ethereum, this is by and large the biggest reason that I believe in its future.
I wanted to invest in a technically sound project that was seeking to re-engineer the internet and the way computing can be architected.
A technically sound blockchain platform would ensure that software engineers and developers would be driven to use it.
And in technology, if developers use it, then the business people will follow.
Thesis: In addition to reducing pollution, we need to build and deploy floating clean-up robots in waterways and canals across the globe.
Plastic bottles are thought to take about 450 years to break down . Leaving plastic waste in landfills is a less than ideal solution.
And besides, a lot of plastic never makes it to the landfill. Over years, large plastic bottles are broken down into tiny and even microscopic “microplastics” that are so embedded within the ocean, sand, dirt and topsoil of our world that they will never be removed.
There are a few questions to consider before jumping in:
What should we really do with plastics, styrofoams, and other non-decomposable garbage?
How will we identify, collect, and transport these plastics to a safe and environmentally friendly final resting place?
From the landfill and back again:
Answering these questions is simple in theory, but more challenging in practice.
Plastics are made from petroleum, which comes from deep underground. Petroleum is like natural gas, crude oil, etc. At a chemical level, these petroleum resources are made of hydrocarbon polymers that can be used to make plastic.
Given that plastics are made from petroleum, which comes from underground, the logical place to put plastic waste is back where they came from – deep underground, between 3000 and 6000 feet. (That’s around 1 mile underground) 
It only makes sense that we should put them back where they came from. And perhaps the heat and pressure of Earth’s crust could accelerate the speed to which these waste products are transitioned back to crude petroleum.
But pulling petroleum out of the ground is a challenging business. Humans leverage advanced petroleum engineering technologies to extract these hydrocarbons. Imagine how much complicated engineering and drilling would be required to replace tons of plastic garbage materials back where they came from.
It would be next to impossible, and absolutely unaffordable. Its not going to happen.
Analogous to setting up for a party, when you set up and get ready for a party, you go buy food and drinks for your guests, setup decorations, plan games and activities, send out invitations and logistic information, etc.
Preparing for a party is fun and requires a bit of planning and effort.
After the party is over, however, there is a similar amount of un-fun effort required to clean up. There are dirty dishes and trash to be cleaned and disposed of. There may be spilled drinks on carpet or furniture, and you have to use something like Resolve carpet cleaner to restore them to their original condition.
The work required to clean up after the party is significantly more difficult than setting up for the party.
The problem with pollution in our world is that there is no one cleaning up after the party. And understandably so. Its a difficult, challenging, dirty, and expensive task.
Besides that, there’s no incentive to do so. Humans don’t want to clean up after other people all the time, yet everyone knows that all of us contribute to pollution.
This predicament is called the “Tragedy of the Commons”.
When you have a party at your house, you live in the direct vicinity of the mess that is left after a party. In the environment and world, however, people are able to artificially remove themselves from the mess created by society (aka pollution).
Although we have some vague perception that the great pacific garbage patch exists, because we don’t encounter it day to day as individuals, we are able to go on living our lives without feeling too bad about it.
Despite the complex, energy intensive manufacturing processes that are performed to make gasoline for cars and plastic goods, the vehicle exhaust, garbage and microplastics that enter our atmosphere and ocean have nowhere to go except accumulate in the environment.
Humans seem to have accepted the fact that these are are just left there.
But this is changing in some areas.
Some states like California and Hawaii have taken measures to prevent new plastic and garbage from entering the environment. Some popular prevention measures include smog inspections, no plastic grocery bags, no plastic straws, etc.
Prevention is good, but cleaning up is still needed.
We need an efficient and scalable way to clean up the Earth.
In order to relieve humans of the burden, perhaps we can leverage machines to take on the majority of plastic and garbage collection tasks associated with removing pollution from the environment.
There is good news. Humans have started doing this already. In Xi’an, China, there is a machine the size of a skyscraper whose sole purpose is to filter and purify the air.
Its extremely exciting to see humans embarking on these types of developments. Although, as there is no natural incentive to build these (due to the Tragedy of the Commons), perhaps governments can create artificial incentives, offering contracts to engineering and development contractors to build similar skyscrapers.
But how about plastic waste? How might we begin to remove plastics from the environment?
To make any meaningful change, we must start somewhere. China began with the noble mission of reducing air pollution, and has built air filtration skyscrapers.
To focus on removing plastics from the environment, targeting the ocean is a great place to start.
Cleaning the oceans with autonomous boats.
To remove pollution from the oceans, we need solar-powered autonomous boats whose sole purpose is dragging filtration systems through the ocean, and collecting plastic pollution.
Identifying plastic material from organic material and avoiding biological life will be important. Perhaps some sort of artificial intelligence image recognition could help identify plastic waste in the water.
Where to begin? Logically, the best places to deploy these robots are in near the areas of primary pollution – harbors, water ways, sewage outputs, etc.
Take the Ala Wai boat harbor in Honolulu, Hawaii.
The harbor connects the ocean to the Ala Wai canal.
A walk along the Ala Wai canal and a glance into the water will provide the onlooker with a glance at dirty water, old chairs, plastic bags, floating bottles, and more.
In a place as beautiful as Hawaii, its very sad to see any amount of garbage floating around.
And unfortunately, that water unfortunately carries bacteria, sewage, garbage, and more into the gorgeous turquoise waters surrounding the island of Oahu, and is ultimately dispersed across the entire world.
Remember, all the oceans are connected… despite different areas having different names, there is truly only one ocean on Earth. I saw a comedian on Instagram talk about the fact that there is truly only 1 ocean, and it made me think of this.
In terms of next steps, we need to find someone to build the robotic floating garbage collectors. It won’t be an easy task, but it is 100% possible.
Using an autonomy infrastructure tool such as Applied Intuition might help with the development of the software.
How much water can be filtered?
We should take a small area of the global water system like the Ala Wai canal, and deploy robotic cleaning ships here as a test. By measuring length * width we can calculate surface area, then performing various depth-measurements, we can take average depth and use this to calculate an estimate of total volume.
The throughput of each robotic boats will have an estimate of gallons per hour, or gallons per day etc.
In a perfect system, the boats will charge via some sort of docking station, or even run on solar power. They will need to be incredibly energy efficient, with no need to propel themselves too fast, they can remain largely stationary.
These may then be scaled up to larger ships that cruise across the ocean autonomously collecting garbage from the great pacific garbage patch and more.
To remove plastic from the ocean, it is only logical that we have water-filtration boats.
The white-paper presented above simply contains ideas. I am not doing these, I am just an idea maker.
And if you’ve made it to the end of this post, good news! These robots already do exist.
But if they already exist, why is there still so much plastic in the ocean? How much of an impact to these autonomous boats actually have on the reduction of plastic in the ocean?
And then of course a few follow up questions inevitably arise:
After collecting a large amount of the garbage, the important question then becomes what do we do with the plastic? It will be great to remove it from the ocean, but where do we put it as a final resting place?
Follow the Future of Tech email newsletter (below), which is free and focuses on exploring emerging technology.
Get the Future of Technology letter each month. Sign up below.
Success! You're on the list.
Whoops! There was an error and we couldn't process your subscription. Please reload the page and try again.
Say what you will about robots taking over, or artificial intelligence tools replacing human labor in the workforce – the company’s website states that AI is here “to make art creation accessible to the masses”.
And to be frank, the creation process couldn’t be simpler or more intuitive – literally anyone can create something.
Here’s one below that I made, and even added a saccharine title.
Is AI Adoption Accelerating?
The ability to build applications that leverage AI as features yet can be used by the layperson even if they are non-technical means that the adoption of AI might end up happening faster that you might expect.
One can only imagine what artificial intelligence generated music might be like as AI applications expand.
I can’t wait for someone to build a way to create your own music using a process and technique that is as simple and easy as this one.
The purpose of Tesla AI Day is to get the world excited about what Tesla is doing in artificial intelligence beyond cars.
AI day is also a recruiting event for prospective engineers as the company ramps up hiring.
Key Takeaways from Tesla AI Day 2021
Make useful AI that people love, and is unequivocally good. – Elon Musk
Vertical integration is a common theme in the presentation. For both software, hardware, neural net training, and more. This means that Tesla builds and designs a large percentage of their technology in house.
The company is able to auto label data sets as well as create simulation data sets with unlimited scenarios for training the neural network.
DOJO is Tesla’s supercomputer designed for one purpose – training neural networks. It will be in use and available next year.
The neural net architecture resembles the visual cortex of an animal.
They will build a humanoid robot (see Tesla Bot at right)
TLDR: skynet is born? Hopefully not. Although Elon said that human-level superintelligence is certainly possible, both the car and the Tesla Bot are examples of building “narrow AI” to avoid AI being misaligned with humans.
FSD beta version 9
FSD (Full-self-driving) is the autonomous system that is deployed to all cars, which customers can purchase for around $10,000.
The often debated fact that Tesla does not use LIDAR, as do many other autonomy oriented companies like GM cruise or Google Waymo, means that its cars use only cameras to gather data about the surroundings and navigate the world. Although the company mentioned plans to upgrade the cameras, the current cameras are still more than good enough.
The philosophy behind this decision is that roads were built for human eyes to see and navigate. Therefore, the cars should be able to gather sufficient data to navigate autonomously using only cameras.
Elon jokingly stated that because of this, someone could technically wear a T-shirt with a stop sign on it, and the car would stop. But ultimately, the company seems confident that cameras will be sufficient.
“It’s clearly headed to way better than human. Without a question.” – Elon Musk
Note: Tesla cars are not yet fully autonomous. Drivers still need to keep their attention and focus on the road at all times. 
Tesla still has yet to reach High Driving Automation level of autonomy (known as Level 4 Autonomy)
FSD driver-assist benefits:
Navigate on Autopilot
Auto Lane Change
Traffic Light and Stop Control
Neural Net Architecture
There are 8 cameras surrounding the video that capture images of the real world. Tesla’s system uses these images to create a 3D reconstruction of the scene in “vector space”.
Using these images and vector space rendering, the system makes predictions about what the car may encounter a few moments into the future, allowing the car to drive itself safely and without running into anything.
As the neural network improves as it is trained on more and more data, Tesla is slowly building a brain-like neural net that resembles the visual cortex of an animal.
The presenters mentioned that everything Tesla is building is fundamentally country agnostic. Although they are optimizing the neural net models for the US at this point, they will be able to extrapolate to other countries as well in the future.
The ability to plan allows the car to predict and make changes about what other cars are doing on the road in real time.
Predictive and planning themed capabilities aside, the upper limit of the neural network has enough power to remember all of the roads and highways on planet Earth.
The presentation spent a significant amount of time diving into the specifics of Tesla’s Neural Net Architecture. For specifics, watch the replay of the 2021 AI day livestream.
Training Neural Networks – Data Required
Every time a human driver gets inside a Tesla, they are helping to train the neural network. Although this may make an incremental improvement, this is not enough training data.
These networks have hundreds of millions of parameters – it is incredibly important to get as many data sets as possible to create 3D renderings in vector space.
Millions of labels are needed, and each piece of data is essentially just a small video clip. Associated with each clip, you have the actual image/video data, odometer information, GPS coordinates, and more.
Tesla needs millions of vector space data sets to train these neural networks. In the spirit of vertical integration, there was formerly a team at Tesla that tediously labeled all of that data. But manual labeling proved to be too slow – there is a better way.
Auto Labeling Data Sets
Tesla developed an auto labeling system, allowing them to generate extremely large training data sets much faster for training the neural network. The auto labeling mechanism is extremely important.
“Without auto labeling, we would not be able to solve the self driving problem.” – Elon Musk
In addition to real-world data sets from camera footage, Tesla also is creating simulations of traffic scenarios.
It is like a video game, where Tesla Autopilot is the player. Simulation is helpful when data is difficult to source, difficult to label, or is in a closed loop.
Algorithms are able to create the simulation scenarios. These algorithms analyze where the system is failing, and then create more data around the failure points to allow the neural network to learn, improve, and handle those scenarios better in the future.
Elon specifically discouraged the use of machine learning because it is extremely difficult, and largely not the right solution for most use cases.
Project Dojo, Tesla’s Supercomputer
Dojo is the name of the neural network training system. Like a training Dojo.
Given all the data and simulations required, there is a demand for speed and capacity in AI neural network training. This is where Dojo comes in.
Currently, it is difficult to scale up bandwidth and reduce latencies, because processors have not been traditionally designed for training neural nets.
This is why Tesla invented the DPU.
DPU – Dojo processing unit. Whereas CPUs and GPUs are not designed to train neural networks, the DPU is designed to train neural networks.
The goal is to achieve the best possible AI training performance, supporting larger complex models while being power efficient and cost effective. Elon said it will be available next year.
This effectively enhances the AI software system, improving FSD.
Dojo leverages a distributed compute architecture.
It is apparently capable of an exaFLOP, which mean is can do a super high number of calculations per second, way more than your average computer. It is a supercomputer, after all…
Tesla will also make Dojo available to other companies that want to train their own neural networks, effectively building a platform for improving neural networks. This feels like an optimum opportunity to apply the “as-a-service” business model to the world of artificial intelligence and neural network training. By licensing out the use of Dojo, Tesla may be able to create yet another revenue stream for the company.
Thay have reportedly innovated in these chips in a way that means there are no roadblocks to extremely high bandwidth.
The software stack is completely vertically integrated. They build everything in house.
As the Dojo computer and neural network data sets improve the neural network, it is likely that the company will deploy the improved brain-like software upgrades via their over-the-air software updates.
Hardware and Computer Chips
One of the biggest goals is minimize latency, and maximize frame rate. These metrics may be familiar to you if you are involved in video games & graphics.
There is a computer in the car that runs the neural network that has been trained by the massive data sets discussed above.
Allegedly, the computer chip for Tesla’s Full Self-Driving system are produced by Samsung. 
The importance of computer chips to Tesla cannot be overstated. Various computer chips are used in all areas of the vehicle – even in the typically non-tech intensive parts of a car: computerized airbags, seat belts, doors and door handles, etc.
Given the reliance on them, the computer chip and semiconductor shortage is an issue across the globe is certainly a hurdle to resolve. 
Tesla Bot – known endearingly by Elon as “Optimus subprime” – a 5 foot 8 inch, 125 pound humanoid robot.
Given that the Tesla car is already essentially a robot, the Tesla Bot will simply use all the same technologies, in a device with a shape like that of a human.
It will make use of all the same tools that Tesla has in the car… such as 8 cameras, FSD computer, etc.
Elon was unfortunately reluctant to share any specific use cases, other than stating vaguely that it will do boring, repetitive, and dangerous tasks that humans do not want to do.
There are still many unknowns. Will the Tesla Bot have features similar to Siri or Amazon Alexa / Echo?
In addition to being a large automaker, Tesla is showing that they are very much a robotics, artificial intelligence, and software company.
“Be careful whose advice you buy, but be patient with those who supply it.” -Mary Schmich
To understand the future (in any subject) and keep up with latest in emerging technology, there is a broad strategy that is immensely beneficial:
Follow “Who” is building and funding emerging tech.
Follow and learn from where smart people in are investing their time, effort, energy, money, and other resources to have an impact on these technologies becoming real.
Why? Because those most knowledgeable people in a field often have early access to data and information to use in their endeavors. Keeping track of what smart people are doing allows you to benefit from their information access.
Although information feels more accessible than ever, top researchers know about ground-breaking studies before everyone else. Breakthrough research papers are often not widely discussed and are missed by the headlines. Data also can take time before being published.
Venture Capitalists fund companies that no one has heard of; they have perspectives and hypothesis that most of us have not considered. They seek the wisdom of experts, and use that to make investment decisions.
I’m not an expert but I try to really know what the experts think and keep up to date as the experts change their mind. – Tim Urban, talking about AI.
Identify “What” is new and obscure.
Mark Andreesen calls it the ‘What do the nerds do on nights and weekends?’ test.
Said in a similar way: “what the smartest people in the world do on the weekends is what everyone else will do during the week in 10 years.”
What are the nerds talking about and working on that the greater population of people is not even aware of? Video games is a great example. Most people never would have thought that being a professional gamer was a viable way to and earn money by livestreaming on platforms like Twitch. In fact, many people in 2021 probably still don’t even realize this.
Identifying influential and intelligent people who’s ideas are worth spreading is somewhat subjective. There isn’t a sure way to find the brilliant minds of a given area, but a few things to look for include:
Track record of success.
Which people have founded of been an early employee at successful companies? Which angel investors have had successful exits with their portfolio companies?
Network of other influential people in their circle.
Contrarian, not dedicated to mainstream ideas and conventional wisdom.
Go against consensus:
“What you listen to and who you listen to is what you become.” – Gary Vee, recent post on LinkedIn.
In his book Zero to One as well as his talks on YouTube, Peter Thiel shares his favorite interview question: “What important truth do very few people agree with you on?”.
This is not an easy question to answer.
To invest successfully, being able to think from contrarian viewpoints is extremely important.
Holding a hypothesis about a business or about the world that is against the consensus of the general population creates the risk of being wrong. However, by applying the scientific method, a founder can test whether or not this hypothesis is indeed true.
Holding contrarian viewpoints means betting on something that is underrated and undervalued. It means people must disagree with you today, and agree with you in the future.
Although tough to stomach, having people disagree with your hypothesis in the present is a pre-requisite to successful investment thesis.
When done well, spending time and energy on contrarian ideas resembles the “buy low, sell high” approach in investing. When most people regard something as worthless or irrelevant, it is affordable and easy to access. The thing is, most people don’t care about being involved with something that isn’t worth anything today.
A recent example is cryptocurrency. In 2009 or 2010, conversations about cryptocurrency were probably generally ignored. People working on crypto had a unique hypothesis about the future of this technology, and spent time building projects. Vitalik Buterin was building the Ethereum blockchain before most of the world even knew what cryptocurrency was. At the time, cryptocurrency was highly undervalued. Because Vitalik believed there was value in working on building projects in this arena, he spent massive time and energy creating a platform and accumulating skills and experience. Now that the rest of the population is realizing the value of cryptocurrency, Vitalik’s project Ethereum has grown exponentially in value.
This type of growth would not have been possible if Vitalik did not initially pursue and idea that most people would have considered worthless.
Often, the smartest people in the world know things that you don’t. They sit on the boards of highly technical and innovative companies. Their circles include influential people in business internationally.
How and Where to find new ideas?
Its easier said than done, and there really isn’t a single way to discover emerging trends.
Start with thinking about commonly held beliefs and accepted truths, then flipping them around to find areas where the majority may be wrong.
Follow people on Twitter. Being able to read the real-time thoughts of someone that has figured out how to start and launch successful tech companies might give you ideas about how you can do the same. Paul Graham, who has written timeless essays that dispense wisdom for technology founders, Tweets quite often. His essays on business, software, and startups are second to none.
Read subreddits. Despite the large amount of trolls, misinformation, and time-wasting content on this website, Reddit is a great way to obtain a general understanding of a topic by reading forum-based threads with what everyone is saying about a topic. Find the small communities with a dedicated following, and become a contributor.
Listen to interviews and podcast appearances with noteworthy people.
Set specific Google Alerts. For example, setting a Google Alert for Gwynn Shotwell, COO of SpaceX, might help you stay up to date with the excitement in the space travel industry such as rapid point-to-point rocket travel. Some of the greatest business minds don’t have a huge online presence. Setting a Google Alert for the words “Warren Buffett” will help you stay ahead of any big moves that Berkshire Hathaway has made, for example.
Following the Future of Tech letter can help you identify macro trends and insights on crypto, biotech, space travel, technology, and the future.
Exploding Topics may help you keep track of where there is greater interest in specific Google searches.
Every piece of knowledge acquired is just one data point – not everything should be acted upon. Accumulation of knowledge and insights comes with slow and gradual realization of how much you don’t know. I will leave you with this: As Socrates said, “I know that I know nothing”.
Reach out to me on Twitter @espressoinsight and let me know your thoughts.
Driving a car is the MOST dangerous thing we do every day – 40,000 people die in cars each year.
Humans are really bad drivers.
To get a driver’s license, you’re given a 25 question multiple choice test at the DMV and then get behind the wheel.
Humans don’t work towards being excellent drivers the way they train for a marathon, study for medical school, or practice an instrument.
Poor driving is amplified by distraction – checking phone notifications, texts, social feed, etc. How much can the average person be expected to maintain focus with their eyes off the road?
Autonomous vehicles could save tens of thousands of human lives per year.
Get the Future of Technology letter each month. Sign up below.
Success! You're on the list.
Whoops! There was an error and we couldn't process your subscription. Please reload the page and try again.
The image below shows a graph of the advancement and sophistication of autonomous vehicles as time and technology moves forward.
Progress of AV’s hits an inflection point where quick progress displays itself as a steep learning curve, slowly approaching an asymptotic limit of perfectly flawless autonomous driving.
As advancement of AV’s approaches this limit, theoretically, system will never be perfect, but it will surely become good enough that the chances of a collision by an autonomous vehicle with another object is practically zero.
This is the point at which vehicles are 99.9999…..% safe, highlighted as the green line. As systems continue to be developed, we expect the “march of nines” above to approach closer and closer to 100%.
There is a big difference between a small fender bender and a fatal collision.
Before self driving technology progress reaches a point where it is statistically unlikely enough that a collision will ever occur on a road, an autonomous system will need to reach a point where the risk of fatal collision is practically zero.
This may be mitigated by incorporating risk avoidance technologies such as slowing down in high-traffic areas, or even designing fleets of cars that are able to communicate from one to the other.
Although autonomy progress has been drawn as a sigmoidal curve above, there may be an argument that the actual progress would look more like a logarithmic curve, if there were no time of slow progress before the inflection point.
In either case, self driving cars continue get better. Some companies have already built autonomous vehicles that feel safer than human driven cars. But these systems are still not entirely ready for the roads.
Humans do not accept AVs unless they are 100% safe, even if they are safer than human drivers. It’s not that humans feel nonchalantly towards the 40,000 people that dies in car crashes each year, it more that humans have extremely high expectations of technology.
Despite the fact that our cell phones send information through the air, we get frustrated when internet speeds are slow and it takes us a few seconds longer to get an answer from Google. A lack of complacency isn’t exactly a negative thing, it promotes technological advancement.
Any non-zero number of self driving car crash fatalities is absolutely unacceptable. The infamous av-Uber crash in Phoenix, Arizona was a tragic nightmare. Ultimately, autonomous vehicle technology must be perfect before humans will accept it.
Does this highlight some principle that is distinct to human mentality? Here are two examples:
Example 1: Humans have irrational fears
I have a few close friends who choose not to surf or go in the ocean because they are afraid of sharks. This is socially acceptable. But I have never met a single person that avoids riding in an automobile out of fear.
To be fair, transportation is pretty much mandatory for a lot of things in life, whereas going swimming in the ocean is trivial and not a requirement.
Why does a fear of sharks continue to be so disproportionally high among humans, compared to driving, which is orders of magnitude more dangerous?
Example #2: Humans are borderline incompetent at most things…
And our only hope is to create tools to help us accomplish the things we need to do. Expecting a human to drive a car is like expecting someone to prepare and serve a full course dinner without any of the tools that exist in a kitchen. While it is surely possible that someone might be able to build a fire without matches and maintain a consistent temperature with which to cook their food, it is extremely likely that they burn the food and making an awful tasting meal. Without tools that help us cook, we’re incompetent. With tools like utensils and appliances, most people still have a hard time successfully preparing a meal. Even with the most advanced stovetop and cookware, cooking is difficult and takes just the right amount of time and patience to get right.
Transportation is no different. Humans were incompetent at all forms of transportation before railroads and the combustion engine. With engines and automobiles, we’re still awful drivers.
A car is just a tool. It is a solution to the slow transportation problem.
Some people drive cars for fun. Most people drive cars because they have the human centric need to move around from one place to another at their free will. Autonomous vehicles will make transportation more safe and effective.
Get the Future of Technology letter each month. Sign up below.
Success! You're on the list.
Whoops! There was an error and we couldn't process your subscription. Please reload the page and try again.
According to the National Safety Council, over 40,000 people were killed in vehicle-related incidents in 2018. During the previous 3 years, there were more than 120,000 total fatalities.
Artificial intelligence image synthesis is now able to produce realistic images from simple, hand-drawn shapes.
Nvidia’s Artificial Intelligence tool to create Artwork
The AI based artwork tool is built by Nvidia. The tool is called GauGAN, and is available for anyone to use online for free on Nvidia’s AI playground.
Although there are several implications of this technology, but the free Beta version of the application is fun and allows non-artsy people (like me) to unleash our creative side.
How does GauGAN work?
GauGAN works using a machine learning system that’s known as a Generative Adversarial Network, which uses a statistical approach that allows two agents, known as the generator and the discriminator, to engage in an optimization based competition.
This process is a type of unsupervised learning for AI.
This technique ultimately creates more accurate, high-resolution renderings of photos from hand drawn shapes.
As a user, you apply filters and select various labels from which the system will apply a style transfer algorithm to modify the color composition of basic single-colored areas and turn them into more photorealistic scenes.
How will AI Art Creation tools be used?
For digital graphic related disciplines, GauGAN is already being used by artists to build rapid prototypes of scenes and conceptual designs.
The time saving potential that tools like GauGAN will enable are huge.
For the world of video game development, the same is true. As video game creators like Fortnite embark on advancing their in-game creative mode, similar tools could be added to help players make quick mock-ups of maps and environments.