In this work, we identify the "2D-Cheating" problem in 3D LLM evaluation, where these tasks might be easily solved by VLMs with rendered images of point clouds, exposing ineffective evaluation of 3D LLMs' unique 3D capabilities. We test VLM performance across multiple 3D LLM benchmarks and, using this as a reference, propose principles for Figure 1: Example of 2D-Cheating. With rendered better assessing genuine 3D understanding. We images of the point cloud, VLMs could easily solve also advocate explicitly separating 3D abilities some 3D tasks, and even outperform 3D LLMs.

The secret behind the hi-tech future of sushi lies in an unremarkable building in the backstreets of Osaka. Inside, empty plastic cups and plates adorned with scrunched-up wet paper – to replicate the weight and texture of scallops – make their way along a conveyer belt. To one side, concealed behind a plastic screen, technicians monitor data on computer screens, the specifics of their work deemed off-limits to the Observer and a small group of journalists granted rare access to the development "studio" belonging to Sushiro, the leading force in Japan's multimillion dollar sushi train industry. This is where developers make incremental improvements to the restaurant chain's ability to deliver plates of freshly-made sushi to diners' tables with lightning speed, and stay one step ahead of the competition in a sector estimated to be worth 740bn yen (about £4bn). "In the past, diners used to take what they fancied from a free-for-all conveyer belt, but these days most people want to order their favourite sushi," said Masato Sugihara, deputy manager in the IT department at Sushiro's parent company, Food and Life.

No, data is not the new oil. Data is nothing like oil. If someone likes to use the data-is-the-new-oil analogy in conversations about the value of data, chances are that they don't understand why data is valuable, or how to extract value from data. If you like to use this analogy yourself, this article is written for your consideration. Many business leaders today believe that their organization is sitting on a gold mine of data, and they just need to find a way to monetize it.

How can we design reinforcement learning agents that avoid causing unnecessary disruptions to their environment? We argue that current approaches to penalizing side effects can introduce bad incentives in tasks that require irreversible actions, and in environments that contain sources of change other than the agent. For example, some approaches give the agent an incentive to prevent any irreversible changes in the environment, including the actions of other agents. We introduce a general definition of side effects, based on relative reachability of states compared to a default state, that avoids these undesirable incentives. Using a set of gridworld experiments illustrating relevant scenarios, we empirically compare relative reachability to penalties based on existing definitions and show that it is the only penalty among those tested that produces the desired behavior in all the scenarios.

While Amazon continues refining its delivery-by-UAV dream, Yelp is gearing up to test a grounded method to autonomously transport take out. The company is partnering with Marble to use their wheeled drone, which is designed to carry perishable cargo, to try out unmanned food delivery for its Seamless-like Yelp Eat24 service. Specifically, they're sending Marble's robots on trips around SF's Mission and Potrero Hill districts, so lucky Eat24 patrons might get the option to have their grub delivered via the boxy drones -- and their humans. Handlers will "chaperone" the autonomous bots to make sure their initial forays into the world go smoothly. The robots use NVIDIA's TX1 Jetson supercomputers to digest environmental data coming from a suite of cameras, LiDAR, and ultrasonic sensors, the same sensors used by autonomous cars. As they expand delivery to more neighborhoods, Marble and Eat24 will use their drones to map the city's sidewalks and develop optimized routes.

Launched by digital health and weight-loss platform Lose It!, the new feature of an already existing app proposes a simple solution to those who struggle to keep track of their caloric intake: Take a photo of your food, and Snap It will immediately display its calorie count Showing people the caloric value of their foods before they eat them can help modify their eating habits. Some studies have shown that keeping a food journal helps people stick to their diet. And in an effort to fight rising obesity rates, the FDA announced in 2014 that chain restaurants throughout the U.S. will have to post calorie information in their menus (the rule is set to go into effect sometime next year). But the FDA rules won't apply to all restaurants. And food diets are cumbersome, tending to go the way of new year's resolutions.

It must have seemed like a revolutionary idea to diners of the 1920s -- people on roller skates delivering food straight to their driver's side window. Suddenly, the food that families were used to eating around a dinner table now arrived on trays or in bags, ready to be eaten on the go. Little did they know fast food was on the brink of explosion. Nearly 100 years later, restaurants are on the brink of another massive change: robot automation. The best estimates find that up to 50% of jobs could be automated by the late 2030s, with restaurant workers among the most vulnerable to displacement.

The way requires no action plan -- things happen and paths are suggested. Forces at play are many, consequences incalculable. Who can say where a butterfly's fluttering wings will reverberate or if a billionaire drone delivery king will pay to kill? Knowing is not necessary or possible. In this spirit, I head for the Rainforest Roundtable, a themed meeting room in the Clubhouse.

MILJS is a collection of state-of-the-art, platform-independent, scalable, fast JavaScript libraries for matrix calculation and machine learning. Our core library offering a matrix calculation is called Sushi, which exhibits far better performance than any other leading machine learning libraries written in JavaScript. Especially, our matrix multiplication is 177 times faster than the fastest JavaScript benchmark. Based on Sushi, a machine learning library called Tempura is provided, which supports various algorithms widely used in machine learning research. We also provide Soba as a visualization library. The implementations of our libraries are clearly written, properly documented and thus can are easy to get started with, as long as there is a web browser. These libraries are available from http://mil-tokyo.github.io/