We thank the reviewers for giving positive and insightful evaluations of our paper. Specific responses are given below. We will discuss these in our paper. Future work could replace Eureqa inside our framework with more sophisticated SR backends. This Eureqa alternative is optimized for rediscovering existing equations by, e.g., This approach does not seem applicable for discovering new equations so we chose Eureqa.

The universe is noisy and confusing, complex enough to make predictions difficult. Human intelligence and intuition facilitate a basic understanding of some of the activities of the world around us. And they do so well enough to make basic sense of events at the macro space and time scales of the limited perspectives of individuals and small groups. The natural philosophers of human prehistory and early history were mostly limited to common sense rationalization and guess and check. The limitations of these methods, especially for things that are just too big or too complex, are readily apparent in the prevalence and influence of superstition and magical thinking.

We present a novel Auxiliary Truth enhanced Genetic Algorithm (GA) that uses logical or mathematical constraints as a means of data augmentation as well as to compute loss (in conjunction with the traditional MSE), with the aim of increasing both data efficiency and accuracy of symbolic regression (SR) algorithms. Our method, logic-guided genetic algorithm (LGGA), takes as input a set of labelled data points and auxiliary truths (ATs) (mathematical facts known a priori about the unknown function the regressor aims to learn) and outputs a specially generated and curated dataset that can be used with any SR method. Three key insights underpin our method: first, SR users often know simple ATs about the function they are trying to learn. Second, whenever an SR system produces a candidate equation inconsistent with these ATs, we can compute a counterexample to prove the inconsistency, and further, this counterexample may be used to augment the dataset and fed back to the SR system in a corrective feedback loop. Third, the value addition of these ATs is that their use in both the loss function and the data augmentation process leads to better rates of convergence, accuracy, and data efficiency. We evaluate LGGA against state-of-the-art SR tools, namely, Eureqa and TuringBot on 16 physics equations from "The Feynman Lectures on Physics" book. We find that using these SR tools in conjunction with LGGA results in them solving up to 30.0% more equations, needing only a fraction of the amount of data compared to the same tool without LGGA, i.e., resulting in up to a 61.9% improvement in data efficiency.

Artificial Intelligence (AI) has flown beneath the radar for the first six decades of its development, and just recently it's exploded into public consciousness, according to Neil Jacobstein. The AI & Robotics co-chair at Singularity University and former CEO of Teknowledge Corporation believes the combination of humans, AI, and good business processes is disrupting the very ground we stand on in financial technology (Fintech) and beyond as we already use machine learning tools to augment the power of the human brain. At Singularity University's Exponential Finance conference today, Jacobstein pointed to examples like Google's AlphaGo wiping the floor with world champion Lee Sedol, and how quickly the world shifted from a state of shock to seizing the scientific opportunity. Soon after seeing its human champion so thoroughly beaten by a machine, South Korea announced a plan to spend $840 million by 2020 to boost AI research and development (R&D), and to create a research institute where six of the country's highest profile tech companies--Samsung, LG, SK Telecom SKT, KT, Naver, and Hyundai--will each invest up to $3 billion in AI R&D. He also mentioned Facebook's DeepText AI language comprehension and categorization, as well as Viv, saying that calling it another personal assistant misses the point.

DataRobot, the leader in automated machine learning, announced it has acquired Nutonian, Inc., a data science software company specializing in time series analytical modeling. The terms of the acquisition were not disclosed, and the deal is officially closed. Developed in Cornell's Artificial Intelligence Lab by two of the "World's Most Powerful Data Scientists," Nutonian's A.I.-powered modeling engine, Eureqa, powers predictive and prescriptive analytics at global companies, including Audi, Beck's Hybrids, NASA, and RealPage. Eureqa is renowned for its success in time series analytics and for creating easy-to-interpret predictive models in minutes rather than weeks or months. The addition of the Nutonian team and technology helps DataRobot accelerate its mission to bring machine learning to the masses by bolstering its capabilities across different types of modeling problems, particularly those involving time series data.

Advances in machine learning are making security systems easier to train and more flexible in dealing with changing conditions, but not all use cases are benefitting at the same rate. Machine learning, and artificial intelligence, has been getting a lot of attention lately and there's a lot of justified excitement about the technology. One of the side effects is that pretty much everything is now being relabeled as "machine learning," making the term extremely difficult to pin down. Just as the word "cloud" has come to mean pretty much anything that happens online, so "artificial intelligence" is rapidly moving to the point where almost anything involving a computer is getting that label slapped on it. "There is also a lot of hype," said Anand Rao, innovation lead for US analytics at PricewaterhouseCoopers LLC.

Advances in machine learning are making security systems easier to train and more flexible in dealing with changing conditions, but not all use cases are benefitting at the same rate. Machine learning, and artificial intelligence, has been getting a lot of attention lately and there's a lot of justified excitement about the technology. One of the side effects is that pretty much everything is now being relabeled as "machine learning," making the term extremely difficult to pin down. Just as the word "cloud" has come to mean pretty much anything that happens online, so "artificial intelligence" is rapidly moving to the point where almost anything involving a computer is getting that label slapped on it. "There is also a lot of hype," said Anand Rao, innovation lead for US analytics at PricewaterhouseCoopers LLC.

Artificial Intelligence (AI) has flown beneath the radar for the first six decades of its development, and just recently it's exploded into public consciousness, according to Neil Jacobstein. The AI & Robotics co-chair at Singularity University and former CEO of Teknowledge Corporation believes the combination of humans, AI, and good business processes is disrupting the very ground we stand on in financial technology (Fintech) and beyond as we already use machine learning tools to augment the power of the human brain. At Singularity University's Exponential Finance conference today, Jacobstein pointed to examples like Google's AlphaGo wiping the floor with world champion Lee Sedol, and how quickly the world shifted from a state of shock to seizing the scientific opportunity. Soon after seeing its human champion so thoroughly beaten by a machine, South Korea announced a plan to spend 840 million by 2020 to boost AI research and development (R&D), and to create a research institute where six of the country's highest profile tech companies--Samsung, LG, SK Telecom SKT, KT, Naver, and Hyundai--will each invest up to 3 billion in AI R&D. He also mentioned Facebook's DeepText AI language comprehension and categorization, as well as Viv, saying that calling it another personal assistant misses the point.