Mar 31, 2022
Our 91st episode with a summary and discussion of last week’s big AI news!
Outline:
Continue reading “DeepMind Mafia, DishBrain, PRIME, ZooKeeper AI, Instant NeRF” »
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.”
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.
𝐄𝐱𝐩𝐞𝐫𝐭 𝐒𝐚𝐲𝐬 𝐇𝐮𝐦𝐚𝐧𝐬 𝐂𝐨𝐮𝐥𝐝 𝐋𝐢𝐯𝐞 𝐋𝐨𝐧𝐠𝐞𝐫 𝐛𝐲 𝐔𝐩𝐥𝐨𝐚𝐝𝐢𝐧𝐠
Experts believe that with new AI and metaverse technologies, humans could upload to their brain to the web and potentially live longer.
Local consciousness, or our phenomenal mind, is emergent, whereas non-local consciousness, or universal mind, is immanent. Material worlds come and go, but fundamental consciousness is ever-present, according to the Cybernetic Theory of Mind. From a new science of consciousness to simulation metaphysics, from evolutionary cybernetics to computational physics, from physics of time and information to quantum cosmology, this novel explanatory theory for a deeper understanding of reality is combined into one elegant theory of everything.
#CyberneticTheoryofMind #Consciousness #Evolution #Mind #Documentary
Why do industrial robots require teams of engineers and thousands of lines of code to perform even the most basic, repetitive tasks while giraffes, horses, and many other animals can walk within minutes of their birth?
My colleagues and I at the USC Brain-Body Dynamics Lab began to address this question by creating a robotic limb that learned to move, with no prior knowledge of its own structure or environment [1,2]. Within minutes, G2P, our reinforcement learning algorithm implemented in MATLAB®, learned how to move the limb to propel a treadmill (Figure 1).
System sidesteps computing bottleneck in tuning artificial intelligence algorithms.
Closing Gaps in Geometrically Frustrated Symmetric Clusters: Local Equivalence between Discrete Curvature and Twist Transformations.
Siemens and Roboze have announced that they are collaborating to develop workflows dedicated to the industrialization of 3D printing. This includes an emphasis on expanding the use of the technology in energy, mobility, and aerospace. Though the exact nature of the agreement isn’t fully elucidated, it marks a significant shift for both firms.
Siemens is the largest industrial manufacturer in Europe, with a storied history spanning nearly two centuries and annual revenues totaling €62.3 billion, as of 2021. In contrast, Roboze is a comparatively new firm, established in Italy in 2013. The company has since built itself up into a leader in industrial-grade material extrusion 3D printers, earning such customers as Ducati, GE, and the U.S. Army.
The partners do not exactly clarify their intent except to say that they will work together to “increase the productivity, competitiveness and efficiency of manufacturers that have embarked on the path to the future of industry.” They do mention focusing on “digitalization and automation projects”.
