Article -> Article Details
| Title | A Virus-Hunter Talks about Novel Viruses |
|---|---|
| Category | Education --> Colleges |
| Meta Keywords | A Virus-Hunter Talks about Novel Viruses |
| Owner | john mathew |
| Description | |
| Why do near-perfect AI models from labs often fail in the real world? A group of 40 researchers across seven different teams at Google have identified a second major cause for the common failure of machine-learning models. The way AI models are currently being trained is fundamentally flawed. The process used to build most of the machine-learning models we use today can’t tell if they will work in the real world or not. And there lies the problem. Data shift is a known problem but now there’s a new one called underspecification.Why do near-perfect AI models from labs often fail in the real world? A group of 40 researchers across seven different teams at Google have identified a second major cause for the common failure of machine-learning models. The way AI models are currently being trained is fundamentally flawed. The process used to build most of the machine-learning models we use today can’t tell if they will work in the real world or not. And there lies the problem. Data shift is a known problem but now there’s a new one called underspecification.Why do near-perfect AI models from labs often fail in the real world? A group of 40 researchers across seven different teams at Google have identified a second major cause for the common failure of machine-learning models. The way AI models are currently being trained is fundamentally flawed. The process used to build most of the machine-learning models we use today can’t tell if they will work in the real world or not. And there lies the problem. Data shift is a known problem but now there’s a new one called underspecification.Why do near-perfect AI models from labs often fail in the real world? A group of 40 researchers across seven different teams at Google have identified a second major cause for the common failure of machine-learning models. The way AI models are currently being trained is fundamentally flawed. The process used to build most of the machine-learning models we use today can’t tell if they will work in the real world or not. And there lies the problem. Data shift is a known problem but now there’s a new one called underspecification.Why do near-perfect AI models from labs often fail in the real world? A group of 40 researchers across seven different teams at Google have identified a second major cause for the common failure of machine-learning models. The way AI models are currently being trained is fundamentally flawed. The process used to build most of the machine-learning models we use today can’t tell if they will work in the real world or not. And there lies the problem. Data shift is a known problem but now there’s a new one called underspecification.Why do near-perfect AI models from labs often fail in the real world? A group of 40 researchers across seven different teams at Google have identified a second major cause for the common failure of machine-learning models. The way AI models are currently being trained is fundamentally flawed. The process used to build most of the machine-learning models we use today can’t tell if they will work in the real world or not. And there lies the problem. Data shift is a known problem but now there’s a new one called underspecification.Why do near-perfect AI models from labs often fail in the real world? A group of 40 researchers across seven different teams at Google have identified a second major cause for the common failure of machine-learning models. The way AI models are currently being trained is fundamentally flawed. The process used to build most of the machine-learning models we use today can’t tell if they will work in the real world or not. And there lies the problem. Data shift is a known problem but now there’s a new one called underspecification. | |