UC Science Today

Accelerator mass spectrometry, or AMS, has been used for decades for radiocarbon dating and has since been used widely in biomedical research. At the Lawrence Livermore National Laboratory, medical scientist Mike Malfatti says they’re now using smaller AMS technology that’s dedicated to biomedical studies.

"The AMS technology has come a long way since when we first started. Before it was a very big instrument that took up a warehouse-size room. Well, the technology has changed where machines are shrinking. And we’re hoping as things develop further we can have AMS machines that can be used in the clinics."

The National Institutes of Health is funding biomedical AMS work, including developing a test to predict how people will respond to chemotherapeutic drugs.

"This particular collaboration that we’re doing with UC Davis, we’re looking at the different chemotherapeutic drugs, how people respond to them, why some people will respond and some people won’t respond."

Accelerator mass spectrometry, or AMS, has been used for decades for radiocarbon dating and has since been used widely in biomedical research. At the Lawrence Livermore National Laboratory, medical scientist Mike Malfatti says they’re now using smaller AMS technology that’s dedicated to biomedical studies.

"The AMS technology has come a long way since when we first started. Before it was a very big instrument that took up a warehouse-size room. Well, the technology has changed where machines are shrinking. And we’re hoping as things develop further we can have AMS machines that can be used in the clinics."

The National Institutes of Health is funding biomedical AMS work, including developing a test to predict how people will respond to chemotherapeutic drugs.

"This particular collaboration that we’re doing with UC Davis, we’re looking at the different chemotherapeutic drugs, how people respond to them, why some people will respond and some people won’t respond."

Accelerator mass spectrometry, or AMS, has been used for decades for radiocarbon dating and has since been used widely in biomedical research. At the Lawrence Livermore National Laboratory, medical scientist Mike Malfatti says they’re now using smaller AMS technology that’s dedicated to biomedical studies.

"The AMS technology has come a long way since when we first started. Before it was a very big instrument that took up a warehouse-size room. Well, the technology has changed where machines are shrinking. And we’re hoping as things develop further we can have AMS machines that can be used in the clinics."

The National Institutes of Health is funding biomedical AMS work, including developing a test to predict how people will respond to chemotherapeutic drugs.

"This particular collaboration that we’re doing with UC Davis, we’re looking at the different chemotherapeutic drugs, how people respond to them, why some people will respond and some people won’t respond."

Scientists have developed a new deep learning technique that enables robots to learn motor tasks from scratch. Study leader Pieter Abbeel of the University of California, Berkeley says the robot learns based on a reward system, but is given no prior knowledge of its surroundings or how to complete the task, such as screwing a cap onto a bottle.

"If it's doing well, it gets a high reward. If it's doing poorly, it gets a low reward. And over time it figures out what behavior results in high rewards and what behavior results in low rewards. Let's say we want it to screw a cap onto a bottle. We would give it a high reward for bringing the cap very close to the top of the bottle, even higher reward when it's kind of placing it nicely onto the top of the bottle. And then, even higher if it's screwing the cap onto the bottle, and really low if it holds the cap very far away from the bottle."

Abbeel says it’s the first time a robot has learned a new task throughout the entire visual motor path, which takes about three hours of trial and error.

"We want robots to learn from their own trial and error, all the way from pixels coming into the camera to motor torques, so the entire visual motor path."

Scientists have developed a new deep learning technique that enables robots to learn motor tasks from scratch. Study leader Pieter Abbeel of the University of California, Berkeley says the robot learns based on a reward system, but is given no prior knowledge of its surroundings or how to complete the task, such as screwing a cap onto a bottle.

"If it's doing well, it gets a high reward. If it's doing poorly, it gets a low reward. And over time it figures out what behavior results in high rewards and what behavior results in low rewards. Let's say we want it to screw a cap onto a bottle. We would give it a high reward for bringing the cap very close to the top of the bottle, even higher reward when it's kind of placing it nicely onto the top of the bottle. And then, even higher if it's screwing the cap onto the bottle, and really low if it holds the cap very far away from the bottle."

Abbeel says it’s the first time a robot has learned a new task throughout the entire visual motor path, which takes about three hours of trial and error.

"We want robots to learn from their own trial and error, all the way from pixels coming into the camera to motor torques, so the entire visual motor path."

Scientists have developed a new deep learning technique that enables robots to learn motor tasks from scratch. Study leader Pieter Abbeel of the University of California, Berkeley says the robot learns based on a reward system, but is given no prior knowledge of its surroundings or how to complete the task, such as screwing a cap onto a bottle.

"If it's doing well, it gets a high reward. If it's doing poorly, it gets a low reward. And over time it figures out what behavior results in high rewards and what behavior results in low rewards. Let's say we want it to screw a cap onto a bottle. We would give it a high reward for bringing the cap very close to the top of the bottle, even higher reward when it's kind of placing it nicely onto the top of the bottle. And then, even higher if it's screwing the cap onto the bottle, and really low if it holds the cap very far away from the bottle."

Abbeel says it’s the first time a robot has learned a new task throughout the entire visual motor path, which takes about three hours of trial and error.

"We want robots to learn from their own trial and error, all the way from pixels coming into the camera to motor torques, so the entire visual motor path."

Scientists have developed a new deep learning technique that enables robots to learn motor tasks from scratch. Study leader Pieter Abbeel of the University of California, Berkeley says the robot learns based on a reward system, but is given no prior knowledge of its surroundings or how to complete the task, such as screwing a cap onto a bottle.

"If it's doing well, it gets a high reward. If it's doing poorly, it gets a low reward. And over time it figures out what behavior results in high rewards and what behavior results in low rewards. Let's say we want it to screw a cap onto a bottle. We would give it a high reward for bringing the cap very close to the top of the bottle, even higher reward when it's kind of placing it nicely onto the top of the bottle. And then, even higher if it's screwing the cap onto the bottle, and really low if it holds the cap very far away from the bottle."

Abbeel says it’s the first time a robot has learned a new task throughout the entire visual motor path, which takes about three hours of trial and error.

"We want robots to learn from their own trial and error, all the way from pixels coming into the camera to motor torques, so the entire visual motor path."

Why do researchers study astronomy if there’s not always a clear social benefit? Graduate student and planet hunter Lauren Weiss of the University of California, Berkeley says that other than satisfying curiosity, one reason is to someday travel to these planets and ensure our own survival.

"When I think about humanity, and the idea that we evolved from simple animals, to be curious and ask about the universe, and know that there are other planets out there, I think it would be really sad if our species came and went without ever leaving Earth. And that, to really be the next level of intelligent life form in civilization, we need to become space faring, and then that will also help ensure the survival of humanity over billions and billions of years. If we can spread ourselves out, instead of putting all our chips on one pale blue dot."

Weiss specializes in identifying exoplanets around nearby stars.

Why do researchers study astronomy if there’s not always a clear social benefit? Graduate student and planet hunter Lauren Weiss of the University of California, Berkeley says that other than satisfying curiosity, one reason is to someday travel to these planets and ensure our own survival.

"When I think about humanity, and the idea that we evolved from simple animals, to be curious and ask about the universe, and know that there are other planets out there, I think it would be really sad if our species came and went without ever leaving Earth. And that, to really be the next level of intelligent life form in civilization, we need to become space faring, and then that will also help ensure the survival of humanity over billions and billions of years. If we can spread ourselves out, instead of putting all our chips on one pale blue dot."

Weiss specializes in identifying exoplanets around nearby stars.

Why do researchers study astronomy if there’s not always a clear social benefit? Graduate student and planet hunter Lauren Weiss of the University of California, Berkeley says that other than satisfying curiosity, one reason is to someday travel to these planets and ensure our own survival.

"When I think about humanity, and the idea that we evolved from simple animals, to be curious and ask about the universe, and know that there are other planets out there, I think it would be really sad if our species came and went without ever leaving Earth. And that, to really be the next level of intelligent life form in civilization, we need to become space faring, and then that will also help ensure the survival of humanity over billions and billions of years. If we can spread ourselves out, instead of putting all our chips on one pale blue dot."

Weiss specializes in identifying exoplanets around nearby stars.