Intel is invading the physical AI space with a reentry into the robotics market it quit many years ago amid financial struggles.
The robotics strategy is part of the company’s larger plan to establish AI on the “edge,” in which devices have the computing capability to run AI locally. Many devices lack AI capabilities and have to offload processing to the cloud.
The chipmaker said its Intel Series 3 processors are now in 130 edge AI and robotics designs. It also had a design win with SensoryAI, which provides technology for robots that include Ella, a robotic barista made by Crown Digital.
The company’s Core Ultra Series 3 processors are derivatives of chip designs intended for laptops. But Intel has achieved a level of power efficiency for long battery life that allows those chips to be adapted for handheld devices and laptops.
Intel also said it can build advanced robotics chips thanks to its latest manufacturing technologies.
For example, many robotic functions, such as computer vision and real-time controls, can be integrated into a single chip. Previously, functions like graphics and movement and control were distributed among different cores in a chip.
SensoryAI, for example, has a chip architecture that provides the robotic barista — which is more like a robotic arm — with AI capabilities, Intel said.
The main “Avatar” agent handles customers as the main “Ella” agent reasons and executes the task. If Ella encounters errors, it passes on the issue to a Guardian agent, which helps with the recovery. Some issues could include making sense of an order, or cups that might be stuck.
The three agents are embedded in a single piece of Core Ultra Series 3 silicon.
Intel is displaying some of those robots at the Computex trade show in Taiwan. The company shared a video of a humanoid-style robot from the floor in a X.com post
This is not Intel’s first attempt at the robotics market. Intel sold robotics chips and kits when it was a dominant chip player in the field, but curtailed efforts in 2021 after Pat Gelsinger took over as CEO and restructured the company to focus on manufacturing.
Robotics is now back on the menu under new Intel CEO Lip-Bu Tan, who replaced Gelsinger last year. He has restructured to company to focus on high-growth areas that can generate high returns.
A Morgan Stanley study last year indicated the robotics market could be worth $5 trillion by 2050 — and more than 1 billion humanoid robots could be in operation.
Robots are seen to improve human productivity and manufacturing output. For example, they could help factories that are facing labor shortages or be used to complete tasks that are dangerous.
However, challenges remain. There isn’t yet enough real-world data to train robots to do targeted work. And the AI models — generally called world models — they will need are still under development.
Training robots to do a specific job requires a sequence of events to happen in succession without any errors. Companies are still training robots to spot and understand errors, analyze possible resolutions, and take the right corrective action.
Earth has many unique features for a planet, such as a magnetic field, a large moon, and plate tectonics. It’s also the only planet we know of that harbors life. These facts form the basis of the Rare Earth hypothesis, which posits that we haven’t found aliens because other planets in the Galaxy probably don’t have all the right conditions for life.
Another characteristic of Earth is that about 30% of its surface is land and about 70% is ocean. Recently, Columbia University Assistant Professor David Kipping investigated whether the proportion of Earth’s surface covered by dry land versus ocean, or its land fraction, is another reason Earth is habitable not only for simple single-celled organisms, but also for intelligent species like humans.
To test this hypothesis, Kipping created 4 statistical models of planets with different land fractions that intelligent aliens could potentially evolve on. First, he created an equation to describe the likelihood that a planet in its star’s habitable zone has a particular land fraction, known as a probability distribution. Kipping weighted this probability distribution toward the extreme ends, making it more likely that a planet would be covered by a single huge landmass or a single vast ocean than by a mix of both, as on Earth.
Kipping then incorporated this land fraction probability distribution into his statistical models to calculate the probability that a random planet will have that land fraction and host intelligent life. The 4 scenarios Kipping tested were: 1) that intelligent life is more likely to emerge on land-dominated planets, 2) that it’s more likely to emerge on ocean-dominated planets, 3) that it’s more likely to emerge on planets with roughly equal amounts of land and ocean, and 4) that its emergence is independent of a planet’s land fraction.
As a first step in determining the kinds of planets intelligent aliens would tend to emerge on, Kipping used each model to predict the probability that intelligent life would emerge on a planet with the same land fraction as Earth. He then compared these probabilities by calculating the ratios between each value. Because Earth is the only known planet with intelligent life, a model that predicted a greater probability for humanity’s existence on Earth would be more likely to reflect reality.
Kipping considered it strong evidence that a given model was more realistic than another if the ratio between 2 of them was greater than 10, meaning one model was 10 times more likely to predict the existence of Earth and humanity. Kipping found that no comparison of any 2 models passed this threshold. However, the models assuming that intelligent life prefers ocean-dominated planets or planets with a land-ocean balance were 2.5 and 3 times more likely to predict the existence of humanity than the model assuming that intelligent life prefers land-dominated planets. Additionally, the model assuming that intelligent life prefers a land-ocean balance was always more likely to predict humanity than any other model, though marginally.
Kipping also addressed whether finding more planets with intelligent life would affect which model was deemed most realistic, for example, if scientists discovered conclusive evidence of life on Mars in its distant past. Here, Kipping identified 2 complications. First, it’s uncertain how much of Mars’s surface was once covered by water – some estimate it had a land fraction as high as 81%, while others estimate it was as low as 25%. Second, proving that Mars once had life would not prove it once had intelligent life.
Regardless, Kipping reran the models assuming that ancient Mars had a land fraction comparable to Earth’s. Adding this second data point produced ratios similar to those in the earlier Earth-only calculations, meaning it still didn’t make any single model 10 times more likely to predict the existence of humans and Martians, respectively.
Kipping then took the 10-times threshold and reversed the calculations to find what conditions would exceed it. In doing so, he calculated that astronomers would need to find 14 other planets with intelligent life and known land fractions to robustly determine whether intelligent life is more likely to occur on desert planets, ocean planets, balanced planets, or without bias.
Kipping concluded that he can’t yet definitively state whether there is something special about Earth’s land fraction when it comes to producing intelligent species. However, Earth’s existence would suggest that intelligent life is unlikely to favor extreme desert planets, so the Milky Way probably isn’t filled with Tatooines and Jakkus. And while his analysis doesn’t debunk the Rare Earth hypothesis, it does undermine the argument that Earth’s ocean size explains why Earth is rare.
The post Is Earth’s water why intelligent life evolved here? appeared first on Sciworthy.
Login