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Marketing Assistant: Mack Patterson. Cover Designers: Kirsten Sims Artificial Intelligence (AI) is a big field, and this is a big book. We have tried to explore the. Expert Systems. Required Text Books: portal7.info Rich, "Artificial Intelligence", 2nd Edition,. McGraw Hill, 2. Dan portal7.infoson, "Introduction to AI and ES". Introduction to Artificial Intelligence and Expert Systems book. Dan W. Patterson To ask other readers questions about Introduction to Artificial Intelligence.
Abstractions biology deep learning DNA evolution genomics Could deep learning help paleontologists and geneticists hunt for ghosts? When modern humans first migrated out of Africa 70, years ago, at least two related species, now extinct, were already waiting for them on the Eurasian landmass. But there have been growing hints of an even more convoluted and colorful history: A team of researchers reported in Nature last summer , for instance, that a bone fragment found in a Siberian cave belonged to the daughter of a Neanderthal mother and a Denisovan father. The finding marked the first fossil evidence of a first-generation human hybrid. Our knowledge of Denisovans, for instance, is based on DNA extracted from a mere finger bone. Many other ancestral pairings could easily have transpired, including ones that involved hybrid groups from earlier crosses — but they might be practically invisible when it comes to physical evidence. Statistical models have helped scientists infer the existence of a couple of these populations without fossil data: For example, according to research published in late , patterns of genetic variation in ancient and modern humans point to an unknown human population having interbred with Denisovans or their ancestors.
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Readers Also Enjoyed. Goodreads is hiring! If you like books and love to build cool products, we may be looking for you. A computer has no problem these days distinguishing a dog from an airplane or a tree.
But even the best AI is unable to analyze a complex image or scene with anything like the sophistication and nuance that a person brings to the job. Jitendra Malik of the University of California, Berkeley, and 11 other machine-learning experts recently published results from a study that uses a new, meticulously annotated video data set to test the performance of a bleeding-edge deep-learning system.
Deep learning involves the application of artificial neural networks—computer programs that are constructed in a way that is crudely analogous to the networks of neurons in a human brain. Malik and his colleagues wanted to train a network to identify where actions were most likely taking place in video footage, and to do so they combined features of two popular neural networks: I3D [PDF], a descendant of the widely used Inception Network , and Faster-RCNN. There was no attempt to interpret more complicated actions say, shoplifting or applying a choke hold.
And the video segments came from films and television shows, so they presumably show the action clearly, with good lighting and appropriate camera angles. Even so, the deep-learning system tested on the new data set often faltered—identifying, for example, people as smoking when they were, in fact, just holding a phone to their ear.
For example, Axon says that once somebody in a video is tagged, its system can maintain that tag as the subject moves through multiple frames of the video. The most advanced computer-vision systems are able to track figures in this way, but the examples of successful tracking described in the research literature rely on high-quality video footage of people moving in relatively sterile environments.
Nobody has yet demonstrated such tracking using video that was captured in poor lighting by moving cameras with erratic fields of view. Automated video interpretation is a tricky problem in any domain.
But in policing, the demands are positively enormous, and the sorts of errors that AI systems tend to make could have dire consequences. Problems can arise, for example, when an automated image-classification system learns its function from messy, incomplete, or biased data. They collected a huge set of bird images and then crowdsourced the labeling of species.
Using those results, they trained their first species-classification AI. The computer scientists then contacted some real bird experts—from the Cornell Lab of Ornithology—to figure out what was going on.
After painstaking efforts to fix the many errors in the training set, writing new and improved instructions for the crowd workers, and repeating the entire process several times, the designers of Merlin were finally able to release an app that worked reasonably well.
And they continue to improve their AI system by drawing on user data and following up on corrections from experts in ornithology. Photo-illustration: Stuart Bradford Dextro, the New York City—based computer vision startup that was acquired by Axon last year, described a similar approach used with its video-recognition system.
The company debugged its AI creations by continuously identifying false positives and false negatives, retraining its neural-network models, and evaluating how the system changed in response. We can hope that these researchers continue this practice as part of Axon. At the European Conference on Computer Vision this past September, in Munich, AI experts from Axon did describe how their technology fared in an open video-understanding competition , where it was highly ranked.
That competition analyzed YouTube videos, though, so the relevance of these results to police body-cam video remains unclear. And Axon has shared much less about its AI capabilities than has Chinese computer-vision startup Megvii , which regularly submits its image-analysis system to public competitions— and routinely wins.
AI developers often identify where and how their systems break down by evaluating their performance using certain well-established criteria. This is why AI research, particularly in computer vision, leans heavily on domain experts as happened with the Merlin app. A shared set of benchmarks and a set of open contests and workshops where any interested party can participate also foster an environment where problems with an AI system can readily surface.
But as Elizabeth Joh, a legal scholar at the University of California, Davis, argues [PDF], this process is short-circuited when private surveillance-technology companies assert trade secrecy privileges over their software. Obviously, police departments have to procure equipment and services from the private sector.
But AI of the sort that Axon is developing is fundamentally different from copier paper or cleaning services or even ordinary computer software. The technology itself threatens to change police judgments and actions. Imagine, to invent a hypothetical example, that a video-interpretation AI categorized women wearing burqas as people wearing masks.
Prompted by this classification, police might then unconsciously start to treat such women with greater suspicion—perhaps even to the point of provoking those women to be less cooperative.
And that change, which would be recorded by body cams, could then influence the training sets Axon uses to develop future AI tools, cementing in a prejudice that arose initially just from a spurious artifact of the software.
Without independent experts in the loop to scrutinize these automated interpretations, this circular system can rapidly degenerate into an AI that produces biased or otherwise unreliable results. It found a racially biased pattern of erroneous predictions that persisted even after controlling for criminal history and the type of crime committed.
Statistician Kristen Lum and political scientist William Isaac found similar problems with a predictive-policing system called PredPol. They showed that this system produces outcomes that are often biased against black people because of biased training data.