The superbrain that predicts the weather will be in a different kingdom of mind from the intelligence woven into your clothes. The superbrain that predicts the weather accurately will be in a completely different kingdom of mind from the intelligence woven into your clothes. The types of artificial minds we are making now and will make in the coming century will be designed to perform specialized tasks, usually tasks beyond what we can do. Our most important thinking machines will not be machines that can think what we think faster, better, but those that think what we can't think.
Reading all the news from Google I/O may have kept you too busy to keep up with this week's app news. Each week we round up the most important app news along with some of the coolest new and updated apps to help you stay in the loop with everything you need on your phone.Here's what caught our eye this week. After a months long feature by feature cloning of Snapchat, Instagram's finally gets Snapchat's most iconic feature - the lenses, or as Instagram calls them "face filters." There's also a new bottom bar for Home, Calls, Camera, People and Games.
Whether its neural networks, machine learning, fuzzy logic, case-based reasoning or expert systems, AI has the potential to transform the industry. This type of technology was brought into the fortune 500 earlier this week when Intel acquired Nervana Systems, an Indian-American San Diego based startup, who was using this technology to increase operational efficiency in oil exploration. On a macro scale, deep machine learning can help to increase the awareness of macroeconomic trends to drive investment decisions in exploration and production (E&P). Although the adoption of new hard technology such as directional drilling and hydraulic fracturing brought on fracking, the O&G industry needs to continue this trend in today's low-price market to survive.
Todoist's latest upgrade is one that will benefit the busiest of task list-addled users. It's added two-way sync between its task lists and Google Calendar. This means that not only can you see, sort and prioritise tasks from inside your calendar (on the web, or on your app), but that any changes made will also flow back to your Todoist account. It'll also cleverly block off time in your gCal schedule to ensure you bake in time for myriad projects and tasks.
As the world begins to turn away from fossil fuels and depend increasingly on renewable resources, the energy sector is presented with a problem. Texas-based oil and gas company Pioneer Natural Resources has said that using AI could help ensure accurate and optimal drilling locales. Oil and gas companies are dedicating large research teams to the development of AI as for its potential to increase production without the need to hire many more workers, an attractive prospect as crude oil prices continue to be unstable. While the necessary systems needed to transform the renewable and traditional energy industries are still in development, we can expect big changes soon.
Data Preparation for Machine Learning in Vertica Posted on Monday, May 8th, 2017 at 1:05 pm. This blog post was authored by Vincent Xu. Introduction Machine learning (ML) is an iterative process. From understanding data, preparing data, building models, testing models to deploying models, every step of the way requires careful examination and manipulation of the data. This is especially true at the beginning of this cycle where the raw data must be cleaned and prepared for modelling. In this blog, I will briefly walk through all the data-wrangling tools available in Vertica. Anyone who tries to conduct ML in Vertica should take advantage of the following capabilities to help prepare his/her data. Loading data in various formats from a variety of data sources Most data scientists probably agree that one of the trickiest steps is to gather all the data from various sources into one place and convert them into one common format. Table format in a relational database like Vertica is well ...
An unexpected equipment failure can affect offshore producers much the way an unexpected closure of Interstate 10 affects Houston drivers. For offshore operators, lost time means less money to the bottom line. That's bad news for companies and their shareholders as unplanned equipment shutdowns cost billions of dollars each year, potentially driving the cost of producing offshore oil well above today's market prices The safe and economical recovery of future oil and gas resources demands operational efficiency, and this efficiency can be realized only if there are no unplanned downtimes due to equipment failures. I worked with them to develop a curriculum to train engineers to use mathematical modeling, simulation and data processing to capture and use this data for real-time condition and performance monitoring of oil and gas production systems.
The readings can be skewed by extreme temperatures, accidental man-handling, hardware malfunctions, or even a worm that's been accidentally skewered by the device. The stakes and responsiveness are much higher for industrial applications where millions of dollars and human lives can be on the line. Despite the high volume of faulty data and limited processing power at the edge, industrial AI still needs to be incredibly accurate. According to Kodesh, the only way to ensure such high fidelity and performance is to run thousands of algorithms at the same time.
Research Scientist – Bayesian Machine Learning Location: Cambridge - United Kingdom The Schlumberger Gould Research Centre offers a stimulating research environment with real-world problems that push the limits of scientific knowledge. The Schlumberger Gould Research Centre strongly encourages the self-development of its scientists, offers high-end experimental facilities and scientific resources, and maintains strong collaborations with academic and industrial research groups worldwide. Job Description The successful candidate will contribute to aspects of an autonomous drilling system such as diagnosis and control by developing technologies that integrate machine learning techniques and physics-based models, preferably in a Bayesian framework. Responsibilities Develop and apply machine learning and data analytics techniques to support various aspects of an autonomous drilling system (monitoring, diagnosis, control, and planning) Test and validate solutions through simulations and full-scale experiments Keep up to date and expand your knowledge in the field Publish research papers, internal technical reports and patents, and present your work Qualifications PhD degree in mathematics, physics or computer science with elements of machine learning and data analytics Understanding of various machine learning techniques, such as Gaussian processes, reinforcement learning, and Bayesian approaches Preferred experience in one or more of the following: combination of machine learning techniques with physics-based models, application to control or diagnosis, explicit handling of uncertainty Competency in algorithm development and implementation Schlumberger is an equal employment opportunity employer.
GE for GE: Deploy the digital industrial blueprint to improve its own manufacturing operations through Brilliant Factories, internal and external supply chains with the asset management system called the Digital thread, and engineering design with machine-learning modeling software called the Digital twin. GE for World: Enable industrial companies by connecting them to the Industrial Internet, which can save hundreds of millions of dollars in manufacturing downtime and outages or by optimizing entire supply chains. GE for GE: Deploy the digital industrial blueprint to improve its own manufacturing operations through Brilliant Factories, internal and external supply chains with the asset management system called the Digital thread, and engineering design with machine-learning modeling software called the Digital twin. GE for World: Enable industrial companies by connecting them to the Industrial Internet, which can save hundreds of millions of dollars in manufacturing downtime and outages or by optimizing entire supply chains.