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Disruptive business models are often powered by alternative financing. In Part 1 of this series, I discussed how mobile is redefining money and banking and shared some of the dramatic transformations in the global remittance infrastructure.
In this article, we’ll discuss:
AI financial advisors and robo traders
Let’s dive right back in…
Decentralized Lending = Democratized Access to Finances
Peer-to-peer (P2P) lending is an age-old practice, traditionally with high risk and extreme locality. Now, the P2P funding model is being digitized and delocalized, bringing lending online and across borders.
Zopa, the first official crowdlending platform, arrived in the United Kingdom in 2004. Since then, the consumer crowdlending platform has facilitated lending of over 3 billion euros ($3.5 billion USD) of loans.
Person-to-business crowdlending took off, again in the U.K., in 2005 with Funding Circle, now with over 5 billion euros (~5.8 billion USD) of capital loaned to small businesses around the world.
Crowdlending next took off in the US in 2006, with platforms like Prosper and Lending Club. The US crowdlending industry has boomed to $21 billion in loans, across 515,000 loans.
Let’s take a step back… to a time before banks, when lending took place between trusted neighbors in small villages across the globe. Lending started as peer-to-peer transactions.
As villages turned into towns, towns turned into cities, and cities turned into sprawling metropolises, neighborly trust and the ability to communicate across urban landscapes broke down. That’s where banks and other financial institutions came into play—to add trust back into the lending equation.
With crowdlending, we are evidently returning to this pre-centralized-banking model of loans, and moving away from cumbersome intermediaries (e.g. high fees, regulations, and extra complexity).
Fueled by the permeation of the internet, P2P lending took on a new form as ‘crowdlending’ in the early 2000s. Now, as blockchain and artificial intelligence arrive on the digital scene, P2P lending platforms are being overhauled with transparency, accountability, reliability, and immutability.
Artificial Intelligence Micro Lending & Credit Scores
We are beginning to augment our quantitative decision-making with neural networks processing borrowers’ financial data to determine their financial ‘fate’ (or, as some call it, your credit score). Companies like Smart Finance Group (backed by Kai Fu Lee and Sinovation Ventures) are using artificial intelligence to minimize default rates for tens of millions of microloans.
Smart Finance is fueled by users’ personal data, particularly smartphone data and usage behavior. Users are required to give Smart Finance access to their smartphone data, so that Smart Finance’s artificial intelligence engine can generate a credit score from the personal information.
The benefits of this AI-powered lending platform do not stop at increased loan payback rates; there’s a massive speed increase as well. Smart Finance loans are frequently approved in under eight seconds. As we’ve seen with other artificial intelligence disruptions, data is the new gold.
Digitizing access to P2P loans paves the way for billions of people currently without access to banking to leapfrog the centralized banking system, just as Africa bypassed landline phones and went straight to mobile. Leapfrogging centralized banking and the credit system is exactly what Smart Finance has done for hundreds of millions of people in China.
As artificial intelligence accesses even the most mundane mobile browsing data to assign credit scores, blockchain technologies, particularly immutable ledgers and smart contracts, are massive disruptors to the archaic banking system, building additional trust and transparency on top of current P2P lending models.
Immutable ledgers provide the necessary transparency for accurate credit and loan defaulting history. Smart contracts executed on these immutable ledgers bring the critical ability to digitally replace cumbersome, expensive third parties (like banks), allowing individual borrowers or businesses to directly connect with willing lenders.
Two of the leading blockchain platforms for P2P lending are ETHLend and SALT Lending.
ETHLend is an Ethereum-based decentralized application aiming to bring transparency and trust to P2P lending through Ethereum network smart contracts.
Secure Automated Lending Technology (SALT) allows cryptocurrency asset holders to use their digital assets as collateral for cash loans, without the need to liquidate their holdings, giving rise to a digital-asset-backed lending market.
While blockchain poses a threat to many of the large, centralized banking institutions, some are taking advantage of the new technology to optimize their internal lending, credit scoring, and collateral operations.
In March 2018, ING and Credit Suisse successfully exchanged 25 million euros using HQLA-X, a blockchain-based collateral lending platform.
HQLA-X runs on the R3 Corda blockchain, a platform designed specifically to help heritage financial and commerce institutions migrate away from their inefficient legacy financial infrastructure.
Blockchain and tokenization are going through their own fintech and regulation shakeup right now. In a future blog, I’ll discuss the various efforts to more readily assure smart contracts, and the disruptive business model of security tokens and the US Securities and Exchange Commission.
Parallels to the Global Abundance of Capital
The abundance of capital being created by the advent of P2P loans closely relates to the unprecedented global abundance of capital.
Initial coin offerings (ICOs) and crowdfunding are taking a strong stand in disrupting the $164 billion venture capital market. The total amount invested in ICOs has risen from $6.6 billion in 2017 to $7.15 billion USD in the first half of 2018. Crowdfunding helped projects raise more than $34 billion in 2017, with experts projecting that global crowdfunding investments will reach $300 billion by 2025.
In the last year alone, using ICOs, over a dozen projects have raised hundreds of millions of dollars in mere hours. Take Filecoin, for example, which raised $257 million in only 30 days; its first $135 million was raised in the first hour. Similarly, the Dragon Coin project (which itself is revolutionizing remittance in high-stakes casinos around the world) raised $320 million in its 30-day public ICO.
Some Important Takeaways…
Technology-backed fundraising and financial services are disrupting the world’s largest financial institutions. Anyone, anywhere, at anytime will be able to access the capital they need to pursue their idea.
The speed at which we can go from “I’ve got an idea” to “I run a billion-dollar company” is moving faster than ever.
Following Ray Kurzweil’s Law of Accelerating Returns, the rapid decrease in time to access capital is intimately linked (and greatly dependent on) a financial infrastructure (technology, institutions, platforms, and policies) that can adapt and evolve just as rapidly.
This new abundance of capital requires financial decision-making with ever-higher market prediction precision. That’s exactly where artificial intelligence is already playing a massive role.
Artificial Intelligence, Robo Traders, and Financial Advisors
On May 6, 2010, the Dow Jones Industrial Average suddenly collapsed by 998.5 points (equal to 8 percent, or $1 trillion). The crash lasted over 35 minutes and is now known as the ‘Flash Crash’. While no one knows the specific reason for this 2010 stock market anomaly, experts widely agree that the Flash Crash had to do with algorithmic trading.
With the ability to have instant, trillion-dollar market impacts, algorithmic trading and artificial intelligence are undoubtedly ingrained in how financial markets operate.
In 2017, CNBC.com estimated that 90 percent of daily trading volume in stock trading is done by machine algorithms, and only 10 percent is carried out directly by humans.
Artificial intelligence and financial management algorithms are not only available to top Wall Street players.
Robo-advisor financial management apps, like Wealthfront and Betterment, are rapidly permeating the global market. Wealthfront currently has $9.5 billion in assets under management, and Betterment has $10 billion.
Artificial intelligent financial agents are already helping financial institutions protect your money and fight fraud. A prime application for machine learning is in detecting anomalies in your spending and transaction habits, and flagging potentially fraudulent transactions.
As artificial intelligence continues to exponentially increase in power and capabilities, increasingly powerful trading and financial management bots will come online, finding massive new and previously lost streams of wealth.
How else are artificial intelligence and automation transforming finance?
Disruptive Remittance and Seamless Transactions
When was the last time you paid in cash at a toll booth? How about for a taxi ride?
EZ-Pass, the electronic tolling company implemented extensively on the East Coast, has done wonders to reduce traffic congestion and increase traffic flow.
Driving down I-95 on the East Coast of the United States, drivers rarely notice their financial transaction with the state’s tolling agencies. The transactions are seamless.
The Uber app enables me to travel without my wallet. I can forget about payment on my trip, free up my mental bandwidth and time for higher-priority tasks. The entire process is digitized and, by extension, automated and integrated into Uber’s platform (Note: This incredible convenience many times causes me to accidentally walk out of taxi cabs without paying!).
In January 2018, we saw the success of the first cutting-edge, AI-powered Amazon Go store open in Seattle, Washington. The store marked a new era in remittance and transactions. Gone are the days of carrying credit cards and cash, and gone are the cash registers. And now, on the heals of these early ‘beta-tests’, Amazon is considering opening as many as 3,000 of these cashierless stores by 2023.
Amazon Go stores use AI algorithms that watch various video feeds (from advanced cameras) throughout the store to identify who picks up groceries, exactly what products they select, and how much to charge that person when they walk out of the store. It’s a grab and go experience.
Let’s extrapolate the notion of seamless, integrated payment systems from Amazon Go and Uber’s removal of post-ride payment to the rest of our day-to-day experience.
Imagine this near future:
As you near the front door of your home, your AI assistant summons a self-driving Uber that takes you to the Hyperloop station (after all, you work in L.A. but live in San Francisco).
At the station, you board your pod, without noticing that your ticket purchase was settled via a wireless payment checkpoint.
After work, you stop at the Amazon Go and pick up dinner. Your virtual AI assistant passes your Amazon account information to the store’s payment checkpoint, as the store’s cameras and sensors track you, your cart and charge you auto-magically.
At home, unbeknownst to you, your AI has already restocked your fridge and pantry with whatever items you failed to pick up at the Amazon Go.
Once we remove the actively transacting aspect of finance, what else becomes possible?
Extraordinary transformations are happening in the finance world. We’ve only scratched the surface of the fintech revolution. All of these transformative financial technologies require high-fidelity assurance, robust insurance, and a mechanism for storing value.
I’ll dive into each of these other facets of financial services in future articles.
For now, thanks to coming global communication networks being deployed on 5G, Alphabet’s LUNE, SpaceX’s Starlink and OneWeb, by 2024, nearly all 8 billion people on Earth will be online.
Once connected, these new minds, entrepreneurs, and customers need access to money and financial services to meaningfully participate in the world economy.
By connecting lenders and borrowers around the globe, decentralized lending drives down global interest rates, increases global financial market participation, and enables economic opportunity to the billions of people who are about to come online.
We’re living in the most abundant time in human history, and fintech is just getting started.
Abundance Digital Online Community: I have created a Digital/Online community of bold, abundance-minded entrepreneurs called Abundance Digital. This is my ‘onramp’ for exponential entrepreneurs – those who want to get involved and play at a higher level. Click here to learn more.
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From a first-principles perspective, the task of feeding eight billion people boils down to converting energy from the sun into chemical energy in our bodies.
Traditionally, solar energy is converted by photosynthesis into carbohydrates in plants (i.e., biomass), which are either eaten by the vegans amongst us, or fed to animals, for those with a carnivorous preference.
Today, the process of feeding humanity is extremely inefficient.
If we could radically reinvent what we eat, and how we create that food, what might you imagine that “future of food” would look like?
In this post we’ll cover:
CRISPR engineered foods
The alt-protein revolution
Let’s dive in.
Where we grow our food…
The average American meal travels over 1,500 miles from farm to table. Wine from France, beef from Texas, potatoes from Idaho.
Imagine instead growing all of your food in a 50-story tall vertical farm in downtown LA or off-shore on the Great Lakes where the travel distance is no longer 1,500 miles but 50 miles.
Delocalized farming will minimize travel costs at the same time that it maximizes freshness.
Perhaps more importantly, vertical farming also allows tomorrow’s farmer the ability to control the exact conditions of her plants year round.
Rather than allowing the vagaries of the weather and soil conditions to dictate crop quality and yield, we can now perfectly control the growing cycle.
LED lighting provides the crops with the maximum amount of light, at the perfect frequency, 24 hours a day, 7 days a week.
At the same time, sensors and robots provide the root system the exact pH and micronutrients required, while fine-tuning the temperature of the farm.
Such precision farming can generate yields that are 200% to 400% above normal.
Next let’s explore how we can precision-engineer the genetic properties of the plant itself.
CRISPR and Genetically Engineered Foods
What food do we grow?
A fundamental shift is occurring in our relationship with agriculture. We are going from evolution by natural selection (Darwinism) to evolution by human direction.
CRISPR (the cutting edge gene editing tool) is providing a pathway for plant breeding that is more predictable, faster and less expensive than traditional breeding methods.
Rather than our crops being subject to nature’s random, environmental whim, CRISPR unlocks our capability to modify our crops to match the available environment.
Further, using CRISPR we will be able to optimize the nutrient density of our crops, enhancing their value and volume.
CRISPR may also hold the key to eliminating common allergens from crops. As we identify the allergen gene in peanuts, for instance, we can use CRISPR to silence that gene, making the crops we raise safer for and more accessible to a rapidly growing population.
Yet another application is our ability to make plants resistant to infection or more resistant to drought or cold.
Helping to accelerate the impact of CRISPR, the USDA recently announced that genetically engineered crops will not be regulated—providing an opening for entrepreneurs to capitalize on the opportunities for optimization CRISPR enables.
CRISPR applications in agriculture are an opportunity to help a billion people and become a billionaire in the process.
Protecting crops against volatile environments, combating crop diseases and increasing nutrient values, CRISPR is a promising tool to help feed the world’s rising population.
The Alt-Protein/Lab-Grown Meat Revolution
Something like a third of the Earth’s arable land is used for raising livestock—a massive amount of land—and global demand for meat is predicted to double in the coming decade.
Today, we must grow an entire cow—all bones, skin, and internals included—to produce a steak.
Imagine if we could instead start with a single muscle stem cell and only grow the steak, without needing the rest of the cow? Think of it as cellular agriculture.
Imagine returning millions, perhaps billions, of acres of grazing land back to the wilderness? This is the promise of lab-grown meats.
Lab-grown meat can also be engineered (using technology like CRISPR) to be packed with nutrients and be the healthiest, most delicious protein possible.
We’re watching this technology develop in real time. Several startups across the globe are already working to bring artificial meats to the food industry.
JUST, Inc. (previously Hampton Creek) run by my friend Josh Tetrick, has been on a mission to build a food system where everyone can get and afford delicious, nutritious food. They started by exploring 300,000+ species of plants all around the world to see how they can make food better and now are investing heavily in stem-cell-grown meats.
Backed by Richard Branson and Bill Gates, Memphis Meats is working on ways to produce real meat from animal cells, rather than whole animals. So far, they have produced beef, chicken, and duck using cultured cells from living animals.
As with vertical farming, transitioning production of our majority protein source to a carefully cultivated environment allows for agriculture to optimize inputs (water, soil, energy, land footprint), nutrients and, importantly, taste.
Vertical farming and cellular agriculture are reinventing how we think about our food supply chain and what food we produce.
The next question to answer is who will be producing the food?
Let’s look back at how farming evolved through history.
Farmers 0.0 (Neolithic Revolution, around 9000 BCE): The hunter-gatherer to agriculture transition gains momentum, and humans cultivated the ability to domesticate plants for food production.
Farmers 1.0 (until around the 19th century): Farmers spent all day in the field performing backbreaking labor, and agriculture accounted for most jobs.
Farmers 2.0 (mid-20th century, Green Revolution): From the invention of the first farm tractor in 1812 through today, transformative mechanical biochemical technologies (fertilizer) boosted yields and made the job of farming easier, driving the US farm job rate down to less than two percent today.
Farmers 3.0: In the near future, farmers will leverage exponential technologies (e.g., AI, networks, sensors, robotics, drones), CRISPR and genetic engineering, and new business models to solve the world’s greatest food challenges and efficiently feed the eight-billion-plus people on Earth.
An important driver of the Farmer 3.0 evolution is the delocalization of agriculture driven by vertical and urban farms. Vertical farms and urban agriculture are empowering a new breed of agriculture entrepreneurs.
Let’s take a look at an innovative incubator in Brooklyn, New York called Square Roots.
Ten farm-in-a-shipping-containers in a Brooklyn parking lot represent the first Square Roots campus. Each 8-foot x 8.5-foot x 20-foot shipping container contains an equivalent of 2 acres of produce and can yield more than 50 pounds of produce each week.
For 13 months, one cohort of next-generation food entrepreneurs takes part in a curriculum with foundations in farming, business, community and leadership.
The urban farming incubator raised a $5.4 million seed funding round in August 2017.
Training a new breed of entrepreneurs to apply exponential technology to growing food is essential to the future of farming.
One of our massive transformative purposes at the Abundance Group is to empower entrepreneurs to generate extraordinary wealth while creating a world of abundance. Vertical farms and cellular agriculture are key elements enabling the next generation of food and agriculture entrepreneurs.
Technology is driving food abundance.
We’re already seeing food become demonetized, as the graph below shows.
From 1960 to 2014, the percent of income spent on food in the U.S. fell from 19 percent to under 10 percent of total disposable income—a dramatic decrease over the 40 percent of household income spent on food in 1900.
The dropping percent of per-capita disposable income spent on food. Source: USDA, Economic Research Service, Food Expenditure Series
Ultimately, technology has enabled a massive variety of food at a significantly reduced cost and with fewer resources used for production.
We’re increasingly going to optimize and fortify the food supply chain to achieve more reliable, predictable, and nutritious ways to obtain basic sustenance.
And that means a world with abundant, nutritious, and inexpensive food for every man, woman, and child.
What an extraordinary time to be alive.
Abundance-Digital Online Community: I’ve created a Digital/Online community of bold, abundance-minded entrepreneurs called Abundance-Digital.
Abundance-Digital is my ‘onramp’ for exponential entrepreneurs—those who want to get involved and play at a higher level. Click here to learn more.
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Earth’s oceans are having a rough go of it these days. On top of being the repository for millions of tons of plastic waste, global warming is affecting the oceans and upsetting marine ecosystems in potentially irreversible ways.
Coral bleaching, for example, occurs when warming water temperatures or other stress factors cause coral to cast off the algae that live on them. The coral goes from lush and colorful to white and bare, and sometimes dies off altogether. This has a ripple effect on the surrounding ecosystem.
Warmer water temperatures have also prompted many species of fish to move closer to the north or south poles, disrupting fisheries and altering undersea environments.
To keep these issues in check or, better yet, try to address and improve them, it’s crucial for scientists to monitor what’s going on in the water. A paper released last week by a team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) unveiled a new tool for studying marine life: a biomimetic soft robotic fish, dubbed SoFi, that can swim with, observe, and interact with real fish.
SoFi isn’t the first robotic fish to hit the water, but it is the most advanced robot of its kind. Here’s what sets it apart.
It swims in three dimensions
Up until now, most robotic fish could only swim forward at a given water depth, advancing at a steady speed. SoFi blows older models out of the water. It’s equipped with side fins called dive planes, which move to adjust its angle and allow it to turn, dive downward, or head closer to the surface. Its density and thus its buoyancy can also be adjusted by compressing or decompressing air in an inner compartment.
“To our knowledge, this is the first robotic fish that can swim untethered in three dimensions for extended periods of time,” said CSAIL PhD candidate Robert Katzschmann, lead author of the study. “We are excited about the possibility of being able to use a system like this to get closer to marine life than humans can get on their own.”
The team took SoFi to the Rainbow Reef in Fiji to test out its swimming skills, and the robo fish didn’t disappoint—it was able to swim at depths of over 50 feet for 40 continuous minutes. What keeps it swimming? A lithium polymer battery just like the one that powers our smartphones.
It’s remote-controlled… by Super Nintendo
SoFi has sensors to help it see what’s around it, but it doesn’t have a mind of its own yet. Rather, it’s controlled by a nearby scuba-diving human, who can send it commands related to speed, diving, and turning. The best part? The commands come from an actual repurposed (and waterproofed) Super Nintendo controller. What’s not to love?
Image Credit: MIT CSAIL
Previous robotic fish built by this team had to be tethered to a boat, so the fact that SoFi can swim independently is a pretty big deal. Communication between the fish and the diver was most successful when the two were less than 10 meters apart.
It looks real, sort of
SoFi’s side fins are a bit stiff, and its camera may not pass for natural—but otherwise, it looks a lot like a real fish. This is mostly thanks to the way its tail moves; a motor pumps water between two chambers in the tail, and as one chamber fills, the tail bends towards that side, then towards the other side as water is pumped into the other chamber. The result is a motion that closely mimics the way fish swim. Not only that, the hydraulic system can change the water flow to get different tail movements that let SoFi swim at varying speeds; its average speed is around half a body length (21.7 centimeters) per second.
Besides looking neat, it’s important SoFi look lifelike so it can blend in with marine life and not scare real fish away, so it can get close to them and observe them.
“A robot like this can help explore the reef more closely than current robots, both because it can get closer more safely for the reef and because it can be better accepted by the marine species.” said Cecilia Laschi, a biorobotics professor at the Sant’Anna School of Advanced Studies in Pisa, Italy.
Just keep swimming
It sounds like this fish is nothing short of a regular Nemo. But its creators aren’t quite finished yet.
They’d like SoFi to be able to swim faster, so they’ll work on improving the robo fish’s pump system and streamlining its body and tail design. They also plan to tweak SoFi’s camera to help it follow real fish.
“We view SoFi as a first step toward developing almost an underwater observatory of sorts,” said CSAIL director Daniela Rus. “It has the potential to be a new type of tool for ocean exploration and to open up new avenues for uncovering the mysteries of marine life.”
The CSAIL team plans to make a whole school of SoFis to help biologists learn more about how marine life is reacting to environmental changes.
Image Credit: MIT CSAIL Continue reading
Neural networks are powerful things, but they need a lot of juice. Engineers at MIT have now developed a new chip that cuts neural nets’ power consumption by up to 95 percent, potentially allowing them to run on battery-powered mobile devices.
Smartphones these days are getting truly smart, with ever more AI-powered services like digital assistants and real-time translation. But typically the neural nets crunching the data for these services are in the cloud, with data from smartphones ferried back and forth.
That’s not ideal, as it requires a lot of communication bandwidth and means potentially sensitive data is being transmitted and stored on servers outside the user’s control. But the huge amounts of energy needed to power the GPUs neural networks run on make it impractical to implement them in devices that run on limited battery power.
Engineers at MIT have now designed a chip that cuts that power consumption by up to 95 percent by dramatically reducing the need to shuttle data back and forth between a chip’s memory and processors.
Neural nets consist of thousands of interconnected artificial neurons arranged in layers. Each neuron receives input from multiple neurons in the layer below it, and if the combined input passes a certain threshold it then transmits an output to multiple neurons above it. The strength of the connection between neurons is governed by a weight, which is set during training.
This means that for every neuron, the chip has to retrieve the input data for a particular connection and the connection weight from memory, multiply them, store the result, and then repeat the process for every input. That requires a lot of data to be moved around, expending a lot of energy.
The new MIT chip does away with that, instead computing all the inputs in parallel within the memory using analog circuits. That significantly reduces the amount of data that needs to be shoved around and results in major energy savings.
The approach requires the weights of the connections to be binary rather than a range of values, but previous theoretical work had suggested this wouldn’t dramatically impact accuracy, and the researchers found the chip’s results were generally within two to three percent of the conventional non-binary neural net running on a standard computer.
This isn’t the first time researchers have created chips that carry out processing in memory to reduce the power consumption of neural nets, but it’s the first time the approach has been used to run powerful convolutional neural networks popular for image-based AI applications.
“The results show impressive specifications for the energy-efficient implementation of convolution operations with memory arrays,” Dario Gil, vice president of artificial intelligence at IBM, said in a statement.
“It certainly will open the possibility to employ more complex convolutional neural networks for image and video classifications in IoT [the internet of things] in the future.”
It’s not just research groups working on this, though. The desire to get AI smarts into devices like smartphones, household appliances, and all kinds of IoT devices is driving the who’s who of Silicon Valley to pile into low-power AI chips.
Apple has already integrated its Neural Engine into the iPhone X to power things like its facial recognition technology, and Amazon is rumored to be developing its own custom AI chips for the next generation of its Echo digital assistant.
The big chip companies are also increasingly pivoting towards supporting advanced capabilities like machine learning, which has forced them to make their devices ever more energy-efficient. Earlier this year ARM unveiled two new chips: the Arm Machine Learning processor, aimed at general AI tasks from translation to facial recognition, and the Arm Object Detection processor for detecting things like faces in images.
Qualcomm’s latest mobile chip, the Snapdragon 845, features a GPU and is heavily focused on AI. The company has also released the Snapdragon 820E, which is aimed at drones, robots, and industrial devices.
Going a step further, IBM and Intel are developing neuromorphic chips whose architectures are inspired by the human brain and its incredible energy efficiency. That could theoretically allow IBM’s TrueNorth and Intel’s Loihi to run powerful machine learning on a fraction of the power of conventional chips, though they are both still highly experimental at this stage.
Getting these chips to run neural nets as powerful as those found in cloud services without burning through batteries too quickly will be a big challenge. But at the current pace of innovation, it doesn’t look like it will be too long before you’ll be packing some serious AI power in your pocket.
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