Software Ate The World, Now AI Is Eating Software
By Martijn van Attekum, Jie Mei and Tarry Singh
Introduction
Marc Andreessen famously said that “Software is eating the world” and everyone gushed into the room. This was as much a writing on the wall for many traditional enterprises as it was wonderful news for the software industry.
Still no one actually understood what he meant.
To make his point he stated this example:
"Today, the world’s largest bookseller, Amazon, is a software company — its core capability is its amazing software engine for selling virtually everything online, no retail stores necessary. On top of that, while Borders was thrashing in the throes of impending bankruptcy, Amazon rearranged its web site to promote its Kindle digital books over physical books for the first time. Now even the books themselves are software."
This was 2011.
Marc Andreessen
TechCrunchInterestingly, Andreessen also said the following:
"I, along with others, have been arguing the other side of the case...We believe that many of the prominent new Internet companies are building real, high-growth, high-margin, highly defensible businesses."
Little did Andreessen envision that the same software industry could be at risk of being eaten.
Fast forward to 2019 and the very same software industry is nervous. Very very nervous!
And the reason is AI.
Especially for those who haven’t bulked up their AI warchest.
Acceleration Wave (2009 - 2019) - When Software Started Eating the World
Andreessen was right.
The companies that embraced software in 2011 are the current market leaders in their respective fields, and the top 5 market capitalization companies worldwide in the second quarter of 2019 are all offering some type of software solutions (ycharts.com).
Concurrently, the period since 2011 has shown an unprecedented growth in the developments in AI. Although several key ideas about AI have been around for long, a number of processes have accelerated their potential use.
First, computing power, in particular for specialized AI chipsets, has vastly increased.
Second, the amount of training data for AI algorithms is exploding with the advent of data lakes and a fully connected internet-of-things world, expanding AI domains and decreasing the costs to train algorithms.
Third, a large number of technological bottlenecks (such as vanishing gradients) have been solved over the last few years, massively increasing accuracy and applicability of existing algorithms.
Lastly, the decrease in costs for cloud storage and computing plus the facilitation of distributed collaborative working, made combining highly specialized knowledge easier than ever before.
The extent in which Andreessen’s cherished software companies are weaving AI into their products is however often limited. Instead, a new slew of start-ups now incorporates an infrastructure based around the above mentioned AI-facilitating processes from their very foundation.
HyperAcceleration Wave (2019 - 2030) - AI Has Started Eating Software
Driven by an increase in efficiency, these new companies use AI to automate and optimize the very core processes of their business. As an example, no less than 148 start-ups are aiming to automate the very costly process of drug development in the pharmaceutical industry according to a recent update on BenchSci.
Likewise, AI start-ups in the transportation sector create value by optimizing shipments, thus vastly reducing the amount of empty or idle transports.
Also, the process of software development itself is affected. AI-powered automatic code completion and generation tools such as TabNine, TypeSQL and BAYOU, are being created and made ready to use.
Let’s quickly look at a few example applications of this hyperacceleration wave:
Automating the coding process
by having TabNine autocomplete your code with AI!
DeepTabNine
TabnineIt is trained on around 2 million files from code repository GitHub. During training, its goal is to predict each token given the tokens that come before it. To achieve this goal, it learns complex behaviors, such as type inference in dynamically typed languages.
Once Deep TabNine developers realized the parallel between code and natural language processing, they implemented the existing GPT-2 tool which uses the Transformer network architecture.
The inventor of this tool is Jacob Jackson, an undergraduate student and ex-OpenAI intern who quickly realized this idea and created a software tool for it.
Getting answers to any question about your medical data
As AI will create the query to get the answer for you!
Here, a group of medical researchers created a tool that you can ask literally any questions on medical data and the AI generates a customized SQL query that is then used to retrieve the relevant data from the database.
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