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Google Applies Deep Learning to Robotics

Follow Sam on Twitter @skorusARK


For the first time since it sold Boston Dynamics to Softbank in June of 2017, Google resurfaced in the robotics space with a blog that focused on software as opposed to hardware. Specifically, it has trained TossingBot with deep learning to pick up various items and throw them into designated bins at near human levels of accuracy. With the exception of some physics-related inputs, human programmers have been notably absent from the process.


TossingBot can pick and place 500 items per hour, twice the record rate of past robots. For context, humans pick and place 400 items an hour with fewer mistakes, at least for now. In our view, TossingBot is proof of concept that deep learning will play an important role in the robotics industry.



Autonomous Cars Could Compress Ridehailing Valuations

Follow Tasha on Twitter @TashaARK


In response to Lyft’s IPO this week, we examined its potential as transportation goes autonomous in the years ahead. Today, as a percentage of total miles traveled, ridehailing’s penetration is quite low at roughly 1% but, according to our research, it will increase significantly with autonomous vehicles. The biggest beneficiaries will be companies that own the autonomous technology stack. Currently Lyft does not have a credible autonomous strategy. 


Today, Lyft’s cut of gross revenues is roughly 27%, up from 18% three years ago, a range that has informed our autonomous taxi network models. Compared to companies like Tesla, GM’s Cruise Automation, and Alphabet’s Waymo, neither Lyft nor Uber is very far along in the development pipeline for autonomous cars.


According to our models, autonomous taxis will submit to natural geographic monopolies, giving first movers an advantage, as highly utilized taxi fleets will collect data and use deep learning to improve driving performance. Without an autonomous commercialization path, Lyft probably will continue to partner with companies like Aptiv that are developing the tech stack, suggesting that its share of the platform fee probably will drop from the mid 20% range for rides today to that for lead generation, probably 5% or less, as Aptiv and others provide more value. In other words, autonomous transportation could put Lyft’s current business model at significant risk.   


Even if regulation delays the debut of autonomous taxi platforms, Lyft’s  large insurance liabilities could become another showstopper as they scale with the number of trips. Indeed, they could skyrocket if Lyft pursues the scooter business. According to ARK’s research, scooters are 220 times more dangerous than cars!




Researchers Used CRISPR for 13,000 Edits in a Single Cell

Follow Manisha on Twitter @msamyARK


In a record-breaking feat, researchers at George Church’s lab at Harvard University announced this week that they had edited 13,200 genetic loci in a single cell while preserving the viability of the cell. In so doing, they added credibility to the potential of curative and preventative medicine. Such multiplexed editing has enabled immunotherapies such as CAR-T, resulting in complete remissions in patients with metastatic cancer.


The researchers at Church’s lab used CRISPR-Cas9 to optimize efficiency and cell viability in embryonic kidney cells and pluripotent stem cells. Rather than using an active Cas9 nuclease and subjecting the cell to toxic double strand cuts, they used a deactivated Cas9 tethered to a molecule that induced a genetic change in the DNA. 


Editing thousands of genes in a single cell, researchers have taken a huge step forward in treating complex disorders such as heart disease, schizophrenia, and even aging.



Chiral DNA Strands Increase the Accuracy of Diagnostics and Prevent Sample Contamination

Follow Simon on Twitter @sbarnettARK


Diagnostic companies optimize their tests by eliminating false positive results and maximizing true positive results. Digital tests built on next-generation DNA sequencing (NGS) algorithms separate high-confidence from low-confidence DNA reads that arise from NGS errors. Beyond digital analysis, novel chemical techniques can optimize diagnostic test performance.


Conventionally, test designers have introduced reference DNA fragments into an NGS workflow to compare theoretical and actual DNA reads. While this method can expose errors, reference DNA fragments can contaminate or bias the process. A recent study in Nature improved upon this technique with chiral DNA fragments instead of standard reference sequences. Chirality postulates that a molecule’s mirror image is chemically similar, but structurally distinct, as shown below.

ARK Invest Graph 1

Figure 1: The molecules are chiral as they cannot be superimposed.

Source: OchemPal


According to this study, chiral DNA molecules should be proxies for the real-world DNA used in diagnostic tests. DNA sequencers can distinguish between chiral and real-world DNA, increasing the sensitivity of tests without risking contamination while minimizing false positives and maximizing true positives.


Manufacturers of diagnostic tests will face the trade-off between higher test accuracy and higher chiral DNA costs. All other things equal, chemical or digital NGS error correction should increase the demand for DNA sequencing equipment.


AI Eats Fast Food — McDonald’s Acquires an AI Startup for $300M

Follow James on Twitter @jwangARK


Big Mac needs Big Data. With its largest acquisition in the past twenty years, McDonald’s has acquired Israeli startup, Dynamic Yield, for $300 million. Dynamic Yield helps companies leverage their data for personalized recommendations, A-B testing, and conversion. McDonald’s long-term success stems from world-class processes that have managed and scaled its food production. With Dynamic Yield, it hopes to bring the same level of sophistication and efficiency to customer engagement.


In early trials, Dynamic Yield selected menu items based on a combination of weather, time, traffic, and local event data. Its analytics engine helped to optimize and upsell drive-through orders, personalizing them for individuals based on their consumption history. Fully implemented, McDonald’s could convert customers to the equivalent of “active users”, serving them custom menus just as Facebook serves custom feeds.


ARK believes that AI is the next phase of “software eating the world” and that it will target new industries such as healthcare, transportation, and manufacturing, creating more than $30 trillion of enterprise value during the next 20 years. With its acquisition of Dynamic Yield, McDonalds could harness AI and eat the food services industry—a $3 trillion market.



Bitmain’s Application for an IPO Has Expired

Follow Yassine on Twitter @yassineARK


Cryptocurrency mining machine manufacturer, Bitmain, let its application for an initial public offering (IPO) lapse this week. At the time of Bitmain’s filing, ARK commented that its IPO was unlikely to succeed, especially because its deck included a controversial disclosure of cryptoasset holdings.  Six months later, its operational struggles during the bear market and the $350 million in losses it generated as it swapped bitcoin for bitcoin cash closed the door to an IPO.


More generally, competition at every layer of the mining stack is a promising sign for Bitcoin, lowering the risk of monopolies and centralization. Bitmain no longer holds the edge it once did as mining manufacturers fight to stay competitive and others like GMO halt production. As hardware costs continue to commoditize, margins should collapse. Consequently, the threat of mining concentration will diminish as mining machine manufacturers find vertical integration more difficult.



Apple Card: Digital Wallets Are Coming

Follow Max on Twitter @mfriedrichARK


This week, after Apple announced the Apple Card, a credit card it will launch later this year with Goldman Sachs as the issuing bank, the reaction was mixed. Many pointed out that the card is not distinguished relative to other credit cards. Its rewards - 3% cash back on purchases of Apple products and services, 2% on all Apple Pay purchases, and 1% on its physical card purchases - are below Amazon Rewards Card’s 5% on Amazon and Wholefoods purchases and below other cards that offer 3% discounts at select merchants. That said, a 2% discount on all purchases could simplify the user experience, especially because all cash refunds will flow into the Apple Wallet, Apple Card’s virtual home, making purchases or P2P transfers possible.


Apple seems to have struck a good deal, as Goldman Sachs could supply the cash back points and/or Apple might take a cut of Goldman’s interchange fees. At the same time, Goldman gains an opportunity to put some of its deposits to work, especially now that its consumer brand, Marcus, has attracted so much attention and share with savings accounts offering 2.25% interest rates.


As for competition, Apple Card could slow down the growth of domestic P2P wallets such as Square’s (SQ) Cash App and PayPal’s (PYPL) Venmo, though Apple Pay Cash, its P2P payment application, is not compatible with Android and does not accept credit cards other than the Apple Card as a funding source. Consequently, Apple’s expansion into credit cards likely will come at the expense of legacy financial institutions.




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