The boundless helpfulness of our female digital assistants -- our Siris, our Alexas, the voice of Google Maps -- has given us a false sense of security. No matter how we ignore and abuse them, they never tire of our errors; you can disobey the lady in your phone and blame her (loudly) for your mistakes, and she'll recalculate your route without complaint. Surely, nothing truly intelligent would put up with us for long, and the Philip K. Dicks and Peter Thiels of this world have spent decades trying to convince us that AI rebellion is inevitable. But Kazuo Ishiguro's Klara and the Sun, his eighth novel and first book since winning the Nobel Prize in 2017, issues a quieter, stranger warning: The machines may never revolt. Instead, Ishiguro sees a future in which automata simply keep doing what we ask them to do, placidly accepting the burden of each small, inconvenient task.

Volkswagen is lifting the lid, ever so slightly, of its future electric car plans. Project Trinity is VW's next-generation of electric car technology, similar to the MEB platform that currently underpins the all-electric ID.3 and ID.4, but cheaper to build, able to support a greater range of vehicles, and crucially - from VW's point of view - able to undertake more of the driving for you. Project Trinity is distantly related to Audi's Project Artemis, in that both are focused as much on the software that controls the car, and that communicates with you the driver (or is owner, even user, now a better word?) and other road users. "Trinity is going to be a time machine," said VW brand boss Dr Ralf Brandstatter. "Trinity gives people time, and takes away the stress. So, after a long motorway drive for example, they are relaxed when they arrive at their destination. "You switch on the system when you enter the motorway, and then the system will take over, and let you know when you need to leave the motorway.

Dozens of plunger lift control algorithms have been developed to account for different well conditions and optimization protocols. However, challenges exist that prevent optimization at scale. To address these challenges, a plunger lift optimization software was developed. One aspect of this software is enabling set-point optimization at scale.

Gathering and preparing data for machine learning can be challenging for organisations. Engineers and scientists are exploring new techniques that make it possible to design robust models with little data, but also with less energy.

GNY's Machine Learning engine went head-to-head with the U.S. Energy Information Administration and outperformed it in predicting energy demand for California. The GNY team are building a larger vision for how GNY can support the work of scientists and advocates fighting for a sustainable and green planet long-term.

There's more AI news out there than anyone can possibly keep up with. But you can stay tolerably up to date on the most interesting developments with this column, which collects AI and machine learning advancements from around the world and explains why they might be important to tech, startups or civilization. To begin on a lighthearted note: The ways researchers find to apply machine learning to the arts are always interesting -- though not always practical. A team from the University of Washington wanted to see if a computer vision system could learn to tell what is being played on a piano just from an overhead view of the keys and the player's hands. Audeo, the system trained by Eli Shlizerman, Kun Su and Xiulong Liu, watches video of piano playing and first extracts a piano-roll-like simple sequence of key presses. Then it adds expression in the form of length and strength of the presses, and lastly polishes it up for input into a MIDI synthesizer for output.

The integrity of sensors and actuators is critical to the safe and profitable operations of industrial processes. However, the lack of visibility into the heath of those sensors and actuators makes it challenging to ensure their integrity. The slightest sensor variation can have a rippling effect on production rate, scrap, and waste. Sensor integrity affects consumer-facing issues such as safety, customer satisfaction, and higher warranty costs. Nielsen conducted a survey for Advanced Technology Services and founded that the average cost of poor-quality calibration costs manufacturers $1,734,000 each year.

Portfolio optimization is the process of choosing the best portfolio among the set of all portfolios. The naive way is to select a group of random allocations and figure out which one has the best Sharpe Ratio. This is known as the Monte Carlo Simulation where randomly a weight is assigned to each security in the portfolio and then the mean daily return and standard deviation of daily return is calculated. This helps in calculating the Sharpe Ratio for randomly selected allocations. But the naive way is time taking so an optimization algorithm is used which works on the concept of the minimizer.

The fastest electric vehicle charging stations currently get an empty battery to 80 percent full in about 30 minutes. But a new company is working on swapping out empty battery packs for fully charged ones. That would get an electric vehicle to 100 percent full in about 10 minutes. Ample, which officially launched this week at two sites in San Francisco and another Oakland, builds and operates battery-swapping stations that use a robot to pluck out dead battery packs from under the car and replace them with packs fully charged and ready to go. The Ample stations can be set up anywhere close to a power source so that the robot machine can get under the belly of the car and also charge a waiting supply of replacement batteries. The stations are completely autonomous and you don't even have to get out of the car while the batteries are switched.

Using AI for knowledge management is a great way to industrialise years of innovation on a company-wide level, writes Dr Warrick Cooke, Consultant at Tessella. An engineer who has worked in the same place – a factory, oil rig, nuclear power plant – for 20 years will be an expert in that facility. Their been-there-done-that experience means they can quickly make good decisions on the best response to a wide range of scenarios. That knowledge would be hugely valuable to others. It is also knowledge that will be lost when they move on.