Behind these smart drones are well-trained deep-learning models based on Baidu's PaddlePaddle, the first open-source deep-learning platform in China. Like mainstream AI frameworks such as Google's TensorFlow and Facebook's PyTorch, PaddlePaddle, which was open sourced in 2016, provides software developers of all skill levels with the tools, services, and resources they need to rapidly adopt and implement deep learning at scale. PaddlePaddle is being used by more than 1.9 million developers and 84,000 enterprises globally. Industries throughout China are using the platform to create specialized applications for their sectors, from the automotive industry's acceleration of autonomous vehicles to the health-care industry's applications for fighting covid-19. Indeed, the coronavirus pandemic, which has spread over 150 countries and caused a worldwide economic shock, is increasing demands for AI transformation.

The news: An open letter from a growing coalition of AI researchers is calling out scientific publisher Springer Nature for a conference paper it reportedly planned to include in its forthcoming book Transactions on Computational Science & Computational Intelligence. The paper, titled "A Deep Neural Network Model to Predict Criminality Using Image Processing," presents a face recognition system purportedly capable of predicting whether someone is a criminal, according to the original press release. It was developed by researchers at Harrisburg University and was due to be presented at a forthcoming conference. The demands: Citing the work of leading Black AI scholars, the letter debunks the scientific basis of the paper and asserts that crime-prediction technologies are racist. It also lists three demands: 1) for Springer Nature to rescind its offer to publish the study; 2) for it to issue a statement condemning the use of statistical techniques such as machine learning to predict criminality and acknowledging its role in incentivizing such research; and 3) for all scientific publishers to commit to not publishing similar papers in the future.

University of Tokyo create a new integrated three-dimensional circuit architecture for artificial intelligence applications with spiraling stacks of memory modules. Researchers at the University of Tokyo Institute of Industrial Science in Japan stacked resistive random-access memory modules for artificial intelligence (AI) applications in a novel three-dimensional spiral. The modules feature oxide semiconductor access transistors, which boost the efficiency of the machine learning training process. The team further enhanced energy efficiency via a system of binarized neural networks, which restricts the parameters to be either 1 or -1, rather than any number, to compress the volume of data to be stored. In having the device interpret a database of handwritten digits, the researchers learned that increasing the size of each circuit layer could improve algorithmic accuracy to approximately 90%.

Specialized replicated compute accelerators (RCA) are multiplied up by having multiple copies per ASICs, multiple ASICs per server, multiple servers per rack, and multiple racks per datacenter. Server controller can be an FPGA, microcontroller, or a Xeon processor. Power delivery and cooling system are customized based on ASIC needs. If required, there would be DRAMs on the PCB as well. Each ASIC interconnects its RCAs using a customized on-chip network.

Mixed precision training with 8-bit floating point.

Victoria Stodden (vcs@stodden.net) is a statistician and associate professor at the University of Illinois at Urbana-Champaign, IL, USA. This material is based upon work supported by National Science Foundation Award #1941443.

AI researchers from the Allen Institute of Artificial Intelligence and the University of Washington have trained a drone agent with a box on top to catch a range of 20 objects in a simulated environment. In trials, the drone had the lowest catch success rate with toilet paper (0%) and the highest with toasters (64.4%). Other objects included alarm clocks, heads of lettuce, books, and basketballs. Overall, the system's success rate in catching objects outpaces two variations of a current position predictor model for 3D spaces, as well as a frequently cited reinforcement learning framework proposed in 2016 by Google AI researchers. For the study, a launcher threw each object two meters (6.5 feet) toward a drone agent.

A robot dog from Boston Dynamics is now officially available to purchase. Spot, as the machine has been dubbed, will cost $74,500 (approximately £60,000). The canine droid is only available to customers in the United States at the moment, after they make a $1,000 deposit. It is capable of climbing stairs and crossing rough terrain, with the company sending the mechanical pooch into dangerous environments to carry payloads from place to place or collect data from the site. Users can control spot through its controller, which "easy access" to the robot's body posing, walking gaits, obstacle avoidance, and local navigation. Spot can also be set to follow predefined routes.

Amazon has released its first Echo device for use outside of the house, allowing users to take Alexa in their car. The company revealed the device in 2018 but it has finally come to customers in the UK and Ireland. Echo Auto plugs into a car's 12V power outlet or built-in USB port and connects to the in-car stereo via either audio jack cable or Bluetooth to enable the use of voice assistant Alexa inside the vehicle. Users are then able to use Alexa voice commands to control music, check the news, make phone calls or check their schedule without taking hands off the wheel or eyes off the road. The device gets internet connectivity by connecting to a user's smartphone and the Alexa app and using its existing data plan.

Bots are terrorizing World of Warcraft Classic servers, stealing precious resources, monopolizing rare monsters, and inflating the virtual economy with truckloads of illicitly earned gold. Today, WoW Classic developer Blizzard Entertainment announced it has suspended or closed over 74,000 WoW Classic accounts over the last month, many of which were automating gameplay with bots. For months, clusters of bot-driven accounts have trawled around high-level zones, attacking monsters with uncanny precision before rotating toward their next target in robotic 90-degree angles. These in-game characters are operated by scripts, programmed to optimally kill monsters and obtain rare, valuable items that drop from them. Lately, they've been targeting the sought-after Black Lotus, a necessary item for some competitive, high-level play.