By leveraging artificial intelligence to help clean up code, DeepCode aims to become to programming what writing assistant Grammarly is to written communications.
Likened to a spell checker for developers, DeepCode’s cloud service reviews code and provides alerts about critical vulnerabilities, with the intent of stopping security bugs from making it into production. The goal is to enable safer, cleaner code and deliver it faster. Read More
Daily Archives: October 25, 2019
DARPA is betting on AI to bring the next generation of wireless devices online
It’s so seamless you almost never notice it, but wireless communication is the foundation upon which much of modern life is built: it powers our ability to text and make calls, hail an Uber, and stream Netflix shows. With the introduction of 5G, it also promises to lower the barrier to safer self-driving cars and kick off a revolution in the internet of things. But this next leap in wireless technology will not be possible without a key ingredient: artificial intelligence. Read More
The Risks of AutoML and How to Avoid Them
When Google Flu Trends was launched in 2009, Google’s chief economist, Hal Varian, explained that search trends could be used to “predict the present.” At the time, the notion that useful patterns and insights could be extracted from large-scale search query data made perfect sense. After all, many users’ digital journeys begin with a search query — including 8 out of 10 people seeking health-related information. So what could possibly go wrong? The answer is infamous in the business and data science communities. Google Flu was shut down in 2015 after the tool’s forecasts overestimated flu levels by nearly 100% relative to data provided by the Centers for Disease Control. Critics were quick to point to the project as the poster-child for big data hubris — the fallacy that inductive reasoning fueled by copious amounts of data can supplant traditional, deductive analysis guided by human hypotheses.
More recently, organizations have shifted towards amplifying predictive power by coupling big data with complex, automated machine learning (autoML). AutoML, which uses machine learning to generate better machine learning, is advertised as affording opportunities to “democratize machine learning” by allowing firms with limited data science expertise to develop analytical pipelines capable of solving sophisticated business problems. In a Kaggle prediction competition held just a few months back, an autoML engine pitted against some of the best data scientists in the world finished second after leading most of the way. However, these advancements have raised concerns about AI hubris. By commoditizing machine learning for process improvement, autoML once again raises questions about what the interplay between data, models, and human experts should look like. What does all this mean for managing in an AI-enabled world? Read More
An Introduction to Deep Reinforcement Learning
Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine.Thus, deep RL opens up many new applications in domains such as healthcare, robotics, smart grids, finance, and many more. This manuscript provides an introduction to deep reinforcement learning models, algorithms and techniques.Particular focus is on the aspects related to generalization and how deep RL can be used for practical applications. We assume the reader is familiar with basic machine learning concepts. Read More
The Next Big Thing in AI/ML is…
The Next Big Thing in AI/ML is…” as the lead to an article is probably the most overused trope since “once upon a time”. Seriously, just how many ‘next big things’ can there be? Is your incredulity not stretched every time you read that?
It’s tempting to say that writers starting an article in this way should be flogged …except that yours truly did recently start one with “the next most IMPORTANT thing in AI/ML…” Well that’s clearly different isn’t it – almost. Read More
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