While puzzling captchas--from dogs in hats to sliding jockstraps--still exist, most bot-deterring challenges have vanished into the background. As I browse the web in 2025, I rarely encounter captchas anymore. There's no slanted text to discern. No image grid of stoplights to identify. And on the rare occasion that I am asked to complete some bot-deterring task, the experience almost always feels surreal.

While the U.S. has lacked central organizing of its AI, it has an advantage in its flexible tech industry, said Nand Mulchandani, the acting director of the U.S. Department of Defense Joint Artificial Intelligence Center. Mulchandani is skeptical of China's efforts at "civil-military fusion," saying that governments are rarely able to direct early stage technology development. Tensions over how to accelerate AI are driven by the prospect of a tech cold war between the U.S. and China, amid improving Chinese innovation and access to both capital and top foreign researchers. "They've learned by studying our playbook," said Elsa B. Kania of the Center for a New American Security. "Many commentators in Washington and Beijing have accepted the fact that we are in a new type of Cold War," said Ulrik Vestergaard Knudsen, deputy secretary general of Organization for Economic Cooperation and Development (OECD), which is leading efforts to develop global AI cooperation.

In this episode of the O'Reilly Podcast, O'Reilly's Ben Lorica sat down with Eliot Knudsen, Field Architect at Tamr. Lorica and Knudsen discuss the role of prescriptive analytics in driving business change, using feedback to train machine learning algorithms, coalescing various sources of business data, and the importance of explaining your algorithms in order to relay their value. The idea behind prescriptive analytics is that you're combining forecasting ability with a specific process or change that folks want to drive in their business--whether it's how they onboard their customers, whether it's how they negotiate with their suppliers, whether it's how they move different products or materials through their supply chain. Feedback is one of these fascinating things, where ultimately there are different ways that these systems are tuning and learning. You're growing, and based upon data and other heuristics, you're changing how your algorithms predict and fit themselves to these points.

In this episode of the O'Reilly Podcast, O'Reilly's Ben Lorica sat down with Eliot Knudsen, Field Architect at Tamr. Lorica and Knudsen discuss the role of prescriptive analytics in driving business change, using feedback to train machine learning algorithms, coalescing various sources of business data, and the importance of explaining your algorithms in order to relay their value. The idea behind prescriptive analytics is that you're combining forecasting ability with a specific process or change that folks want to drive in their business--whether it's how they onboard their customers, whether it's how they negotiate with their suppliers, whether it's how they move different products or materials through their supply chain. Feedback is one of these fascinating things, where ultimately there are different ways that these systems are tuning and learning. You're growing, and based upon data and other heuristics, you're changing how your algorithms predict and fit themselves to these points.

Department of Cognitive Science University of California, San Diego La Jolla, CA 92093-0515 Abstract Psychophysical and physiological evidence shows that sound localization ofacoustic signals is strongly influenced by their synchrony with visual signals. This effect, known as ventriloquism, is at work when sound coming from the side of a TV set feels as if it were coming from the mouth of the actors. The ventriloquism effect suggests that there is important information about sound location encoded in the synchrony between the audio and video signals. In spite of this evidence, audiovisual synchrony is rarely used as a source of information in computer vision tasks. In this paper we explore the use of audio visual synchrony to locate sound sources. We developed a system that searches for regions of the visual landscape thatcorrelate highly with the acoustic signals and tags them as likely to contain an acoustic source.

In young barn owls raised with optical prisms over their eyes, these auditory maps are shifted to stay in register with the visual map, suggesting that the visual input imposes a frame of reference on the auditory maps. However, the optic tectum, the first site of convergence of visual with auditory information, is not the site of plasticity for the shift of the auditory maps; the plasticity occurs instead in the inferior colliculus, which contains an auditory map and projects into the optic tectum. We explored a model of the owl remapping in which a global reinforcement signal whose delivery is controlled by visual foveation. A hebb learning rule gated by reinforcement learnedto appropriately adjust auditory maps. In addition, reinforcement learning preferentially adjusted the weights in the inferior colliculus, as in the owl brain, even though the weights were allowed to change throughout the auditory system. This observation raisesthe possibility that the site of learning does not have to be genetically specified, but could be determined by how the learning procedure interacts with the network architecture.

In young barn owls raised with optical prisms over their eyes, these auditory maps are shifted to stay in register with the visual map, suggesting that the visual input imposes a frame of reference on the auditory maps. However, the optic tectum, the first site of convergence of visual with auditory information, is not the site of plasticity for the shift of the auditory maps; the plasticity occurs instead in the inferior colliculus, which contains an auditory map and projects into the optic tectum. We explored a model of the owl remapping in which a global reinforcement signal whose delivery is controlled by visual foveation. A hebb learning rule gated by reinforcement learned to appropriately adjust auditory maps. In addition, reinforcement learning preferentially adjusted the weights in the inferior colliculus, as in the owl brain, even though the weights were allowed to change throughout the auditory system. This observation raises the possibility that the site of learning does not have to be genetically specified, but could be determined by how the learning procedure interacts with the network architecture.

In young barn owls raised with optical prisms over their eyes, these auditory maps are shifted to stay in register with the visual map, suggesting that the visual input imposes a frame of reference on the auditory maps. However, the optic tectum, the first site of convergence of visual with auditory information, is not the site of plasticity for the shift of the auditory maps; the plasticity occurs instead in the inferior colliculus, which contains an auditory map and projects into the optic tectum. We explored a model of the owl remapping in which a global reinforcement signal whose delivery is controlled by visual foveation. A hebb learning rule gated by reinforcement learned to appropriately adjust auditory maps. In addition, reinforcement learning preferentially adjusted the weights in the inferior colliculus, as in the owl brain, even though the weights were allowed to change throughout the auditory system. This observation raises the possibility that the site of learning does not have to be genetically specified, but could be determined by how the learning procedure interacts with the network architecture.

Sound localization by the barn owl (tyto alba) measured with the search coil technique.

Sound localization by the barn owl (tyto alba) measured with the search coil technique.