An MIT Technology Review article introduces the man responsible for the 30-year-old deep learning approach, explains what deep machine learning is, and questions whether deep learning may be the last significant innovation in the AI field. The article also touches on a potential way forward for developing AIs with qualities more analogous to the human brain’s functioning.
Should it surprise us that human biases find their way into human-designed AI algorithms trained using data sets of human artifacts? Machine-learning software trained on the datasets didn’t just mirror those biases, it amplified them. If a photo set generally associated women with cooking, software trained by studying those photos and their labels created an even stronger association. https://www.wired.com/story/machines-taught-by-photos-learn-a-sexist-view-of-women?mbid=nl_82117_p2&CNDID=24258719
It’s common for brain functions to be described in terms of digital computing, but this metaphor does not hold up in brain research. Unlike computers, in which hardware and software are separate, organic brains’ structures embody memories and brain functions. Form and function are entangled. Rather than finding brains to work like computers, we are beginning to design computers–artificial intelligence systems–to work more like brains. https://www.wired.com/story/tech-metaphors-are-holding-back-brain-research/
An article at Wired.com considers the pros and cons of making the voice interactions of AI assistants more humanlike. The assumption that more human-like speech from AIs is naturally better may prove as incorrect as the belief that the desktop metaphor was the best way to make humans more proficient in using computers. When designing the interfaces between humans and machines, should we minimize the demands placed on users to learn more about the system they’re interacting with? That seems…
This NY Times article is worth your time, if you are interested in AI–especially if you are still under the impression AI has ossified or lost its way.
Google and others are developing neural networks that learn to recognize and imitate patterns present in works of art, including music. The path to autonomous creativity is unclear. Current systems can imitate existing artworks, but cannot generate truly original works. Human prompting and configuration are required. Google’s Magenta project’s neural network learned from 4,500 pieces of music before creating the following simple tune (drum track overlaid by a human): Click Play button to listen-> Is it conceivable that AI may…
TED talk of possible interest: http://www.ted.com/talks/zeynep_tufekci_we_can_t_control_what_our_intelligent_machines_are_learning?utm_source=newsletter_weekly_2016-10-22&utm_campaign=newsletter_weekly&utm_medium=email&utm_content=talk_of_the_week_swipe Comment I posted there: Here is an interdisciplinary “moon-shot” suggestion that we should at least start talking about, now, before it is too late. Let’s massively collaborate to develop a very mission-specific AI system to help us figure out, using emerging genetic editing technologies (e.g., CRISPR, etc.), ideally how to tweak (most likely) species-typical genes currently constraining our capacities for prosociality, biophilia, and compassion, so that we can intentionally evolve into a sustainable species….
“Once the trained CNN [convolutional neural network] showed solid performance in the simulator, we loaded it onto DRIVE PX [vehicle control computer] and took it out for a road test in the car. The vehicle drove along paved and unpaved roads with and without lane markings, and handled a wide range of weather conditions. As more training data was gathered, performance continually improved. The car even flawlessly cruised the Garden State Parkway.”
Artificial intelligence (AI) is being incorporated into an increasing range of engineered systems. Potential benefits are so desirable, there is no doubt that humans will pursue AI with increasing determination and resources. Potential risks to humans range from economic and labor disruptions to extinction, making AI risk analysis and mitigation critical. Specialized (narrow and shallow-to-deep) AI, such as Siri, OK Google, Watson, and vehicle-driving systems acquire pattern recognition accuracy by training on vast data sets containing the target patterns. Humans…
“Once the trained CNN [convolutional neural network] showed solid performance in the simulator, we loaded it onto DRIVE PX [vehicle control computer] and took it out for a road test in the car. The vehicle drove along paved and unpaved roads with and without lane markings, and handled a wide range of weather conditions. As more training data was gathered, performance continually improved. The car even flawlessly cruised the Garden State Parkway.”