• Student

Activity Feed

Excerpt from the New York TOMs review:

Mr. Legal’s discussion of COIN’s prospects in the legal space highlight the importance of distinguishing what can be automated, such as pattern recognition and classification tasks, from what cannot, such as analysis and interpretation. In fact, one of the Bay Area’s fastest-growing startups, Atrium, makes use of precisely this distinction and maintains its own team of in-house lawyers who augment their work with the company’s proprietary machine learning tools. These tools allow its lawyers to replace many hours of costly searches on WestLaw and LexisNexis, passing the time savings and cost benefits on to clients. An interesting follow-up would be to address whether COIN and startups such as Atrium will ultimately become competitors, substitutes, or a mix of the two.

“A Better Law Firm for Startups.” Atrium, 15 Nov. 2018,

On November 15, 2018, cranberries commented on JP Morgan Chase & Machine Learning :

Excerpt from the New York TOMs review:

Although the author raises the prospect of consolidation within the banking industry due to the impact of machine learning, the rest of the article gives the impression of a trend in the opposite direction. If a chatbot can address a significant portion of 120,000 employee tickets, shouldn’t the order of magnitude improvement in efficiency allow smaller banks to compete at a level that would previously have been impossible? Parallel developments have taken place in the lending industry, with companies such as Affirm leveraging machine learning to make smart lending decisions without the benefit of JPMorgan’s scale. Unless the machine learning models involved rely on deeply trained neural networks, which require up to thousands of GPU-years of training and can be cost-prohibitive for smaller firms, machine learning seems more likely to act as a democratizer that replaces the labor of dozens or hundreds of employees with that of a single computer.

Excerpt from the New York TOMs review:

With his typical flair for the audacious and avant-garde, Mr. Hughes imagines a world in which an airplane consists of a single 3D-printed part. Fascinating though this proposal is, it is a shame that Mr. Hughes does not go farther towards answering his own question with an investigation of how printing costs vary with the size of the equipment. Is it sublinear, linear, or perhaps even exponential? Knowing the answer to this question as well as its history and trends would inform the discussion of how AM at Boeing is likely to proceed. However, Mr. Hughes’ recommendation that Boeing expand its AM testing across the company, progressing towards FAA approval in the process, is highly sensible and will likely surface more of the information needed to understand the shape of technological progression. Towards this end, the investment in Digital Alloys’ multi-metal systems is the most exciting of Boeing’s recent partnerships, and an investigation of Digital’s own progression may allow the author to extrapolate towards Boeing’s likely progress in the next 5-10 years.

On November 15, 2018, cranberries commented on Flying High: GE’s Billion Dollar Bet on Additive Manufacturing :

Excerpt from the New York TOMs review:

Mr. Riddle presents an enlightening and well-researched summary of additive manufacturing’s contributions to GE in the context of the Advanced Turboprop engine. Especially interesting is the article’s case that 3D printing represents a change not only in quantity (the type and cost of materials) but in quality, freeing engineers to think of better designs that would not have been possible with normal manufacturing constraints on assembly. Although the article would ideally have followed through on the impact of reduced material consumption on total cost, it offers an excellent breakdown of the scale of the change in total architecture, weight, and specific fuel consumption.

The article’s recommendations note that 3D printing currently makes the most sense for highly customizable projects, and recommends future investments in unit economics to make mass production more effective. This analysis is spot-on, although a deeper look at the fixed and variable cost breakdown at present, and the advancements needed to make mass manufacturing cost-effective, would have further enhanced Mr. Riddle’s case.

Excerpt from the New York TOMs review:

Mr. Gunju rightly points out that AB InBev’s use of an open innovation craft beer contest, though productive in its own right, solves a different problem from the one that “ales” them: consumer unwillingness to be associated with Big Beer and its representatives. The extent to which this effect predominates among craft-brew diehards is underscored by the case of Wicked Weed, which was pulled from the shelves not because of a deterioration in quality or rise in price, but because the taint of the AB InBev brand repelled brewpubs and craft beer aficionados.

The article’s point that ZX Ventures makes AB InBev more proactive is well taken, but drifts away from the author’s previous argument that brand association is to blame. What will e-commerce delivery systems and beer-rating applications do for consumers who would not be caught dead drinking AB’s products? ZX’s current incarnation addresses the innovator’s dilemma but not the brand crisis, and questions remain whether an incubator such as ZX can solve a problem rooted not in product quality but in marketing. Would it be effective to crowdsource the company’s marketing efforts by, for instance, running a social contest to come up with a DeWalt-style campaign as proposed by Ms. Batt? The execution would be dicey, but since the problem was never the product, the potential for using open innovation in a brand context is the most valuable open question.

Excerpt from the New York TOMs review:

Can breadth of investigation and the wisdom of the crowd overcome depth of research in long-term algorithmic investment performance? Ms. Hypatia’s “Open Innovation in the Investment Management Industry” offers a stimulating discussion of this question through Quantopian, which relies on crowdsourced investing ideas submitted to a free online platform. […]

The article highlights the company’s philosophy of automatically filtering low-quality ideas through statistical testing, but would have greatly benefitted from a discussion of how the algorithms that pass the filter are integrated into a broader portfolio strategy. At top-level quantitative hedge funds such as Two Sigma and D.E. Shaw, the investment model and resulting algorithms form only the first component of a three-tiered approach, with integration, or balancing between different strategies, and execution, conducting trades as quickly and unobtrusively as possible to avoid front-running and adverse market movements, forming the latter two stages.

Furthermore, quantitative hedge funds generally rely on either better infrastructure or more comprehensive information, collected through a combination of data mining and research, to provide them with the edge they require. Can the diversity of crowdsourced ideas outweigh the lack of particular advantages in these two areas, especially since it may be difficult to redesign user-submitted algorithms to make use of extra information? A discussion of the relative importance of these factors would have been much appreciated. […]