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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.

Introduction to Artificial Intelligence and Expert Systems

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.

The Search for Subtle Signatures

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.

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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. But that may not happen. This is hardly the first time that new technologies have come along that demand, in the name of effectiveness and safety, to be independently tested and monitored throughout their development and even after they reach the market. Precedents for handling these situations exist. The U. Food and Drug Administration could be one model. The FDA was established in the early 20th century in response to toxic or mislabeled food products and pharmaceuticals.

And it would probably improve the performance of these systems by institutionalizing the sort of collaborative testing and refinement that has powered the recent AI renaissance. State and local governments need not wait, though, for some new federal regulatory regime, which is unlikely to emerge anytime soon. Seattle has provided a blueprint with its recent Surveillance Technology Acquisition legislation , which requires city departments to conduct community outreach and seek city council approval prior to procuring new surveillance software.

The Search for Subtle Signatures Current statistical methods involve examining four genomes at a time for shared traits.

For instance, such analyses might suggest that a modern-day European shares certain traits with the Neanderthal genome but not a modern-day African.

Artificial Intelligence Finds Ancient ‘Ghosts’ in Modern DNA

The latter, for instance, could have instead bred with a different population, one closely related to Neanderthals but not the Neanderthals themselves.

The new deep learning method is an attempt to do better, by seeking to explain levels of gene flow that are too small for the usual statistical approaches, and by offering a far more vast and complicated range of models to do so. Through training, the neural network can learn to classify various patterns in genomic data based on what demographic histories most likely gave rise to them, without being told how to make those connections.

According to Hawks, there could very well have been dozens. The approach is no longer limited by our imagination. Take one of its most common applications, as an image classifier.

But the dearth of relevant anthropological and paleontological data available forced researchers who wanted to use deep learning to get clever, by creating data of their own. From those simulated histories, the scientists generated a massive number of simulated genomes for present-day people.

They trained their deep learning algorithm on these genomes, so that it learned which kinds of evolutionary models were most likely to produce given genetic patterns. Eventually, the system concluded that a previously unidentified human group had also contributed to the ancestry of people of Asian descent. From the genetic patterns involved, those humans were themselves probably either a distinct population that arose from the interbreeding of Denisovans and Neanderthals around , years ago, or a group that descended from the Denisovan lineage shortly afterward.

A handful of labs in the field have been applying similar methods to address other threads of evolutionary investigation. One research group, led by Andrew Kern at the University of Oregon, has used a simulation-based approach and machine learning techniques to differentiate between various models of how species, including humans, evolved.For a classification algorithm that filters emails, the input would be an incoming email, and the output would be the name of the folder in which to file the email.

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Loss functions express the discrepancy between the predictions of the model being trained and the actual problem instances for example, in classification, one wants to assign a label to instances, and models are trained to correctly predict the pre-assigned labels of a set of examples. The finding marked the first fossil evidence of a first-generation human hybrid.

If the complexity of the model is increased in response, then the training error decreases. 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. Introduction to Artifi The latter, for instance, could have instead bred with a different population, one closely related to Neanderthals but not the Neanderthals themselves.