I got my rig in the back of my Beemer. Professional when I graze, I’m professional when I argue. 40 glass, I’m laughing at that s***, I’ma be roaring at that s***
The experiment also revealed which genres are hardest for AI songwriters to master.
The respondents struggled to spot which pop and country lyrics were written by an AI. And its rock song was so emo that they thought it was written by My Chemical Romance or Nirvana.
Hundreds of books are now free to download.
Springer has released hundreds of free books on a wide range of topics to the general public. The list, which includes 408 books in total, covers a wide range of scientific and technological topics. In order to save you some time, I have created one list of all the books (65 in number) that are relevant to the data and Machine Learning field.
Among the books, you will find those dealing with the mathematical side of the domain (Algebra, Statistics, and more), along with more advanced books on Deep Learning and other advanced topics. You also could find some good books in various programming languages such as Python, R, and MATLAB, etc.
This is the seventh in a series on the impact of the coronavirus on China’s technology sector.
China’s robotics market is forecast to reach US$103.6 billion by 2023, driven by manufacturing, consumer, retail, health care and resource applications.
Chinese robotics companies have seen a surge in demand since the coronavirus outbreak but some believe robot tech is not mature enough for widespread use.
Existing electronic skin (e-skin) sensing platforms are equipped to monitor physical parameters using power from batteries or near-field communication. For e-skins to be applied in the next generation of robotics and medical devices, they must operate wirelessly and be self-powered. However, despite recent efforts to harvest energy from the human body, self-powered e-skin with the ability to perform biosensing with Bluetooth communication are limited because of the lack of a continuous energy source and limited power efficiency. Here, we report a flexible and fully perspiration-powered integrated electronic skin (PPES) for multiplexed metabolic sensing in situ. The battery-free e-skin contains multimodal sensors and highly efficient lactate biofuel cells that use a unique integration of zero- to three-dimensional nanomaterials to achieve high power intensity and long-term stability. The PPES delivered a record-breaking power density of 3.5 milliwatt·centimeter−2 for biofuel cells in untreated human body fluids (human sweat) and displayed a very stable performance during a 60-hour continuous operation. It selectively monitored key metabolic analytes (e.g., urea, NH4+, glucose, and pH) and the skin temperature during prolonged physical activities and wirelessly transmitted the data to the user interface using Bluetooth. The PPES was also able to monitor muscle contraction and work as a human-machine interface for human-prosthesis walking.
Recent advances in robotics have enabled soft electronic devices at different scales with excellent biocompatibility and mechanical properties; these advances have rendered novel robotic functionalities suitable for various medical applications, such as diagnosis and drug delivery, soft surgery tools, human-machine interaction (HMI), wearable computing, health monitoring, assistive robotics, and prosthesis (1–6). Electronic skin (e-skin) can have similar characteristics to human skin, such as mechanical durability and stretchability and the ability to measure various sensations such as temperature and pressure (7–11). Moreover, e-skin can be augmented with capabilities beyond those of the normal human skin by incorporating advanced bioelectronics materials and devices.
Researchers at Bilkent University in Turkey have recently created a small quadruped robot called SQuad, which is made of soft structural materials. This unique robot, presented in a paper published in IEEE Robotics and Automation Letters, is more flexible than existing miniature robots and is thus better at climbing or circumventing obstacles in its surroundings.
“We have been working on miniature robots for almost a decade now,” Onur Ozcan, one of the researchers who carried out the study, told TechXplore. “Even though miniature robots have many advantages, such as being cheap, as they require fewer materials, and the ability to access confined spaces, one of their major drawbacks is their lack of locomotion capabilities, especially on uneven terrain.”
Tiny robots tend to get stuck easily while moving in the surrounding environment, as their height does not allow them to climb or avoid obstacles. Ozcan and his colleagues tried to overcome this limitation by implementing a principle known as ‘body compliance.”
The main idea of artificial neural networks (ANN) is to build up representations for complicated functions using compositions of relatively simple functions called layers.
A deep neural network is one that has many layers, or many functions composed together.
Although layers are typically simple functions(e.g. relu(Wx + b)) in general they could be any differentiable functions.
KIA and corporate cousin Hyundai build some efficient EVs that challenge industry leaders like Tesla. Yes, Elon an his minions are miles ahead in self-driving tech, but the Hyundai Kona EV and KIA Niro EV are world class cars that come close to meeting Elon’s plea to other manufacturers to build compelling electric cars.
The problem is, KIA and Hyundai don’t have a dedicated battery electric platform. Both the Kona and Niro share their underpinnings with hybrid and plug-in hybrid models. A pure battery electric car may be coming soon from KIA, however. Recently, Hyundai said it is planning to bring two new all electric models — the 45 Concept and the Prophesy Concept — to market this year and next. Both will be built on the company’s new Electric Global Modular Platform known internally as E-GMP.
Tesla’s progress with artificial intelligence and neural nets has propelled its Autopilot and Full Self Driving solutions to the front of the pack. This is the result of the brilliant work of a large team of Autopilot directors and staff, including Tesla’s Senior Director of AI, Andrej Karpathy. Karpathy presented Tesla’s methods for training its AI at the Scaled ML Conference in February. Along the way, he shared specific insights into Tesla’s methods for achieving the accuracy of traditional laser-based lidar with just a handful of cameras.
The secret sauce in Tesla’s ever-evolving solution is not the cameras themselves, but rather the advanced processing and neural nets they have built to make sense of the wide range and quality of inputs. One new technique Tesla’s AI team has built is called pseudo-lidar. It blends the lines between traditional computer vision and the powerful point map world of lidar.
Traditional lidar-based systems rely on an array of lidar hardware to provide an unparalleled view of the world around the vehicle. These systems leverage invisible lasers or similar tech to send a massive number of pings out into the world to detect surrounding objects.