In Go, no successful evaluation function for non-terminal positions has ever been found. Therefore, it is not a problem that will be solved with faster search. It pushes the boundaries of what is possible with new algorithms such as Monte Carlo methods. Work on computer Go started in the 1960's, but it was not until 2016 that the AlphaGo program was able to best the second-highest ranking professional Go player.
Cannabis farm production is at an all-time high, but it's unlikely that robots will take over the process anytime soon. The stereotypical weed farm is either a sprawling expanse of crop tended to by free-spirited stoners, or a clandestine basement operation built on information gleaned from online forums. Modern cannabis farm facilities, with their climate controlled grow rooms and automatic irrigation techniques, are a stark departure from pop culture's preconceived notions of what a weed farm looks like. Though far more clinical than its cliché predecessor, the modern cannabis farm still does the bulk of cultivation by hand. Few, if any, other agricultural spaces use human labor over that of a machine's to the degree that cannabis farms do, but the quality-driven nature of weed requires fine motor skills and age-old intuition that technology hasn't adapted to yet. While the agricultural industry has relied on machinery for centuries, automation falls short in the cannabis sphere. The rise in states legalizing marijuana and the 2018 Farm Bill that legalized hemp ushered in a "green rush" of farmers who could grow cannabis, and consumers who could finally buy it.
The researchers ran a model for portfolio optimization on Canadian company D-Wave's 2,000-qubit quantum annealing processor. Consultancy firm KPMG, together with a team of researchers from the Technical University of Denmark (DTU) and a yet-to-be-named European bank, has been piloting the use of quantum computing to determine which stocks to buy and sell for maximum return, an age-old banking operation known as portfolio optimization. The researchers ran a model for portfolio optimization on Canadian company D-Wave's 2,000-qubit quantum annealing processor, comparing the results to those obtained with classical means. They found that the quantum annealer performed better and faster than other methods, while being capable of resolving larger problems – although the study also indicated that D-Wave's technology still comes with some issues to do with ease of programming and scalability. The smart distribution of portfolio assets is a problem that stands at the very heart of banking.
Kobe – Japan's Fugaku supercomputer, the world's fastest in terms of computing speed, went into full operation Tuesday, earlier than initially scheduled, in the hope that it can be used for research related to the novel coronavirus. The supercomputer, named after an alternative word for Mount Fuji, became partially operational in April last year to visualize how droplets that could carry the virus spread from the mouth and to help explore possible treatments for COVID-19. "I hope Fugaku will be cherished by the people as it can do what its predecessor K couldn't, including artificial intelligence (applications) and big data analytics," said Hiroshi Matsumoto, president of the Riken research institute that developed the machine, in a ceremony held at the Riken Center for Computational Science in Kobe, where it is installed. Fugaku, which can perform over 442 quadrillion computations per second, was originally scheduled to start operating fully in the fiscal year from April. It will eventually be used in fields such as climate and artificial intelligence applications, and will be used in more than 100 projects, according to state-sponsored Riken. The supercomputer, which was developed jointly with Fujitsu Ltd., was ranked the world's fastest for computing speed in the twice-yearly U.S.-European TOP500 project for the first time in June, and retained the top spot in November.
In both of these cases, the work that accountants and bank tellers are performing is higher-skilled than the work they were doing before. Sometimes something entirely different happens, and the new jobs rely on fundamentally different skills than the old ones, even though, superficially, they seem similar. The invention of the washing machine greatly cut down on the arduous task of scrubbing clothes by hand. Not only were factory jobs created to make the washing machines, but laundromats became a new convenience, and they created jobs, too. Running a successful laundromat requires someone to be able to run a small business.
"Just to rub it in, a version of AlphaGO, called AlphaZero recently learned to trounce AlphaGo at Go, and also to trounce Stockfish (the world's best chess program, far better than any human) and Elmo (the world's best shongi program, also better than any human). AlphaZero did all this in one day." I was reading "Human Compatible" this week and the above anecdote got me thinking. A computer crushing Chess and Go Grandmasters is impressive and feels ominous, but what does it mean for our everyday jobs? Every year computer chips get smaller and faster (Moore's Law) and experts predict Machine Learning, AI and automation will eviscerate our jobs.
Leddar PixSet is a new publicly available dataset (dataset.leddartech.com) for autonomous driving research and development. One key novelty of this dataset is the presence of full-waveform data from the Leddar Pixell sensor, a solid-state flash LiDAR. Full-waveform data has been shown to improve the performance of perception algorithms in airborne applications but is yet to be demonstrated for terrestrial applications such as autonomous driving. The PixSet dataset contains approximately 29k frames from 97 sequences recorded in high-density urban areas, using a set of various sensors (cameras, LiDARs, radar, IMU, etc.) Each frame has been manually annotated with 3D bounding boxes.
In the retail sector, Amazon increasingly uses AI systems to direct and monitor staff in its warehouses. This has led to several reports of employees being overworked, accusations that Amazon has repeatedly denied. Amazon says that if the AI notices a worker underperforming, he or she gets additional support and training, which comes from a human.
The hoax seems harmless enough. A few thousand AI researchers have claimed that computers can read and write literature. They've alleged that algorithms can unearth the secret formulas of fiction and film. That Bayesian software can map the plots of memoirs and comic books. That digital brains can pen primitive lyrics1 and short stories--wooden and weird, to be sure, yet evidence that computers are capable of more. But the hoax is not harmless. If it were possible to build a digital novelist or poetry analyst, then computers would be far more powerful than they are now. They would in fact be the most powerful beings in the history of Earth. Their power would be the power of literature, which although it seems now, in today's glittering silicon age, to be a rather unimpressive old thing, springs from the same neural root that enables human brains to create, to imagine, to dream up tomorrows.
The standard for Deep Reinforcement Learning in games, following Alpha Zero, is to use residual networks and to increase the depth of the network to get better results. We propose to improve mobile networks as an alternative to residual networks and experimentally show the playing strength of the networks according to both their width and their depth. We also propose a generalization of the PUCT search algorithm that improves on PUCT.
When it comes to imitating human emotion, robots have a long way to go, but researchers at Columbia Engineering's Creative Machines Lab have taken an initial step toward this goal. They designed a robot capable of predicting another robot's intent, essentially placing itself in the shoes (or the little plastic wheels) of the second machine. Here's how engineers designed the experiment: They placed one robot in a 3-foot by 2-foot playpen and programmed it to travel toward any green circle it could see projected on the floor. A large red box obscured the robot's view of some circles, meaning it didn't travel to these spots, even if they were physically closer than others. From above, the observer robot watched its buddy navigate the space for two hours and then began predicting its path.