We promise this isn’t an April Fools’ Day post.
Category: robotics/AI – Page 1,437
The study found that if such a system were put in place nationwide, 94 percent of human operator hours may be affected, which could account for as many as 500,000 jobs.
In a situation where automation is restricted only to Sun Belt states, as rough weather poses a challenge to automation, about 10 percent of worker operator hours will be affected. If automation is deployed nationwide only during the spring and summer months, about half the nation’s trucking hours could go driverless.
“I think the most surprising thing there was that everyone we spoke to basically said ‘yeah, this can be done,’” Vaishnav said.
NVIDIA says their 3D scene rendering AI runs up to 1,000 times faster than older models without sacrificing image quality.
Training datasets are very important for experimenting with varied data to train new AI models. However, many commonly used public data sets contain labeling errors. This makes it challenging to train robust models, particularly for novel tasks. Many researchers use techniques such as employing a variety of data quality control procedures to overcome these shortcomings. However, there is no centralized repository consisting of examples of using these strategies.
Meta AI researchers have recently released Mephisto. It is a new platform to collect, share, and iterate on the most promising approaches to collecting training datasets for AI models. Researchers can exchange unique collecting strategies with Mephisto in a reusable and iterable format. It also allows them to change out components and quickly locate the exact annotations required, minimizing the barrier to custom task creation.
The team uncovers many common pathways for driving a complex annotation activity from concept to data collection in Mephisto. In addition to improving the quality of datasets, Mephisto also enhances the experience of the researchers and annotators who created the data set.
A new approach to in-memory computing proposes a new set up to create an artificial synapse that can both store and process data.
In this blossoming era of AI, efficient computational approaches to processing and storing large amounts of data are required. However, current computer designs have inherent performance limitations.
In recent years, research has been focused on the development of alternative computing architectures that mimic the brain. These devices, called neuromorphic computers, circumvent many of the issues associated with the traditional von Neumann architecture, which has been around since 1945 and is composed of processing and memory units.
So-called energy-based models, which borrow concepts from statistical physics, may lead the way to ‘abstract prediction,’ says Yann LeCun, allowing for a ‘unified world model’ for AI capable of planning.
Mar 31, 2022
Our 91st episode with a summary and discussion of last week’s big AI news!
Outline:
Applications & Business.
Meet the DeepMind mafia: These 18 alumni from Google’s AI research lab are raising millions for their own startups, from climate to crypto.
Nvidia unveils new technology to speed up AI, launches new supercomputer.
Speech and language recognition technology is a rapidly developing field, which has led to the emergence of novel speech dialog systems, such as Amazon Alexa and Siri. A significant milestone in the development of dialog artificial intelligence (AI) systems is the addition of emotional intelligence. A system able to recognize the emotional states of the user, in addition to understanding language, would generate a more empathetic response, leading to a more immersive experience for the user.
“Multimodal sentiment analysis” is a group of methods that constitute the gold standard for an AI dialog system with sentiment detection. These methods can automatically analyze a person’s psychological state from their speech, voice color, facial expression, and posture and are crucial for human-centered AI systems. The technique could potentially realize an emotionally intelligent AI with beyond-human capabilities, which understands the user’s sentiment and generates a response accordingly.
However, current emotion estimation methods focus only on observable information and do not account for the information contained in unobservable signals, such as physiological signals. Such signals are a potential gold mine of emotions that could improve the sentiment estimation performance tremendously.
Raspberries are the ultimate summer fruit. Famous for their eye-catching scarlet color and distinctive structure, they consist of dozens of fleshy drupelets with a sweet yet slightly acidic pulp. But this delicate structure is also their primary weakness, as it leaves them vulnerable to even the slightest scratch or bruise. Farmers know all too well that raspberries are a difficult fruit to harvest—and that’s reflected in their price tag. But what if robots, equipped with advanced actuators and sensors, could lend a helping hand? Engineers at EPFL’s Computational Robot Design & Fabrication (CREATE) lab have set out to tackle this very challenge.
Sky-high labor costs and shortages of workers cause farmers to lose millions of dollars’ worth of produce each year—and the problem is even more acute when it comes to delicate crops such as raspberries. But for now, there’s no viable alternative to harvesting the fruit by hand. “It’s an exciting dilemma for us as robotics engineers,” says Josie Hughes, a professor at CREATE. “The raspberry harvesting season is so short, and the fruit is so valuable, that wasting them simply isn’t an option. What’s more, the cost and logistical challenges of testing different options out in the field are prohibitive. That’s why we decided to run our tests in the lab and develop a replica raspberry for training harvesting robots.”
The Promise of Analog AI
Posted in futurism, robotics/AI
Neural networks keep getting larger and more energy-intensive. As a result, the future of AI depends on making AI run more efficiently and on smaller devices.
That’s why it’s alarming that progress is slowing on making AI more efficient.
The most resource-intensive aspect of AI is data transfer. Transferring data often takes more time and power than actually computing with it. To tackle this, popular approaches today include reducing the distance that data needs to travel and the data size. There is a limit to how small we can make chips, so minimizing distance can only do so much. Similarly, reducing data precision works to a point but then starts to hurt performance.