We brainstormed about 40 different ways to control Salto-1P's orientation including multiple tails, a single multi-degree-of-freedom inertial tail (like the body of the original Raibert hopper and Disney's LEAP robot), and big steerable wings. You mentioned that Salto-1P's duty factor [the amount of time the robot spends touching the ground] is twice as fast as a one-legged cheetah would be. The low duty factor means high accelerations on the ground, and very little time to do any control with the leg. Interestingly, like running animals, Salto gets more efficient the faster it runs until it can't run any faster.
Organizations are leveraging HPC solutions to analyze data and derive actionable intelligence at lightning speeds. Today's organizations are leveraging deep learning, a powerful component of AI, to analyze data and derive actionable intelligence at lightning speeds. HPE is bolstering their HPC platforms with advisory and transformational services, including applications designed to enhance security, agility, and flexibility. The HPE Performance Software Suite--including the HPE Performance Software Core Stack, HPE Insight Cluster Management Utility, HPE SGI Management Suite, and HPE Performance Software Message Passing Interface--is helping organizations accelerate HPC application performance and scale with a "limitless" architecture.
As I mentioned in my first post, the process of creating AI requires knowledge about various machine learning methods and a deep understanding of how they work and what their advantages and disadvantages are. This inductive bias determines the type of data an algorithm will work with effectively and the type it will struggle with. If the equations of the model truly reflect the data (for example, a linear model applied to data generated by a linear process), then any fit will be a correct fit for test data. Any learning algorithm must also be a good model of the data; if it learns one type of data effectively, it will necessarily be a poor model -- and a poor student – of some other types of data.
This is the story of how GE has accomplished this digital transformation by leveraging AI and Machine Learning fueled by the power of Big Data. Bill Ruh, the CEO of GE Digital and the company's Chief Digital Officer, emphasizes the role and importance of data and analytics in the company's transformation. Machine Learning technology, according to Ruh, is critical to making the "digital twin" concept successful. Because there may also be changes over time relative to which variables and models best predict the need for required maintenance, machine learning represents the best technology approach to addressing these requirements.
Candidates learn about the jobs online through outlets like Facebook or LinkedIn and submit their LinkedIn profiles -- no résumé required. For example, the game that tests risk gives users three minutes to collect as much "money" as possible using this system: clicking "pump" inflates a balloon by 5 cents; at any point, the user can click "collect money"; and if the balloon pops, the user receives no money. The "balloon game" measures a candidate's relationship to risk. Unilever had exceptional employees in different roles play the games and used their results as a benchmark to measure new candidates against.
In contrast, most AI methods require very large datasets containing matched pairs of "predictor data" and "criteria data." For example, to model work characteristics causing employee stress, you would need predictor data that measured characteristics that might cause stress, as well as criteria data on these same employees that measured stress levels. Of course, AI is amazingly good at finding treasures of useful information in massive piles of garbage data, but it can't find treasures in data that is entirely composed of garbage. Examples include modeling relationships between applicant characteristics and post-hire retention, job characteristics and employee turnover, and employee work characteristics and absenteeism and healthcare costs.
In this article, I will walk through the steps how you can easily build your own real-time object recognition application with Tensorflow's (TF) new Object Detection API and OpenCV in Python 3 (specifically 3.5). Google has just released their new TensorFlow Object Detection API. I wanted to lay my hands on this new cool stuff and had some time to build a simple real-time object recognition demo. And definitely have a look at the Tensorflow Object Detection API.
"Model performance was most strongly influenced by the diversity of data, basic feature construction and the length of the observation window," wrote Kenny Ng, research staff member in the Center for Computational Health and first author of the study. "In raw form, EHR data are highly diverse, represented by thousands of variants for disease coding, medication orders, laboratory measures, and other data types. The model performed best when window length was below two years, the training data set at least 4,000 patients, data were diverse as possible and data were confined to patients with more than 10 meetings with physicians in two years. First, the approach and methods need to be validated on larger patient data sets from multiple healthcare systems and additional disease targets to better understand the generalizability of the data characteristic impacts on predictive modeling performance," wrote Ng et al.
Though AI is commonly thought of in the realm of wearables and Internet of Things (IoT), investors and startups in key industries like business intelligence and analytics, commerce, cyber-security, fintech and healthcare are making waves with their AI investments. AI is on the verge of penetrating every major industry, making it crucial for savvy IT managers to experiment and implement it into their internal and external facing systems if they want to remain relevant. Within 5 years, a Spiceworks Inc. survey indicated that 61% of businesses will incorporate AI technology into their business analytics, 45% in machine learning and 21% in self-learning robots. But while machine learning doesn't require human input, work performance can be enhanced through actionable data when IT managers stay connected with their technology.
Today, at asset management companies and other financial institutions, there are still large teams of analysts and portfolio managers, sifting through data, developing investment theses and making asset allocation decisions. Let's assume that you use very sophisticated AI-driven models to scan data from not just the market but a whole plethora of other sources to define, implement, monitor, refine and adjust your trading strategies. The kinds of people employed in the industry will change; we will need people who can model data, and others who can validate the models and the results. One hedge fund taking artificial intelligence to the next level is Numerai - which doesn't even employ the AI talent!