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Humans Are the World's Best Pattern-Recognition Machines, But for How Long?
Not only are machines rapidly catching up to — and exceeding — humans in terms of raw computing power, they are also starting to do things that we used to consider inherently human. They can feel emotions like regret. They can daydream. So what is - exactly - that humans still do better than machines?
Quite simply, humans are amazing pattern-recognition machines. They have the ability to recognize many different types of patterns - and then transform these "recursive probabalistic fractals" into concrete, actionable steps. If you've ever watched a toddler learn words and concepts, you can almost see the brain neurons firing as the small child starts to recognize patterns for differentiating between objects. Intelligence, then, is really just a matter of being able to store more patterns than anyone else. Once IBM could build machines that could recognize as many chessboard patterns as a chess grandmaster, the machines became "smarter" than humans.
Artificial intelligence pioneer Ray Kurzweil was among the first to recognize how the link between pattern recognition and human intelligence could be used to build the next generation of artificially intelligent machines. In his latest book, How to Create a Mind: The Secret of Human Thought Revealed, Kurzweil describes how he is teaching artificially intelligent machines to think, based on the stepwise refinement of patterns. According to Kurzweil, all learning results from massive, hierarchical and recursive processes taking place in the brain. Take the act of reading – you first recognize the patterns of individual letters, then the patterns of individual words, then groups of words together, then paragraphs, then entire chapters and books. Once a computer can recognize all of these patterns, it can read and "learn."
The same is true for other fields of endeavor as well, where human "expertise" has always trumped machine "expertise." In a brilliant piece for Medium, Kevin Ashton recently analyzed “how experts think." It turns out patterns matter, and they matter a lot. A star football quarterback needs to recognize all kinds of patterns – from the type of defense he's facing, to the patterns his receivers are running, to the typical reactions of defenders. All of this, of course, has to happen in a matter of nanoseconds, as a 300-pound lineman is bearing down on you, intent on ripping you limb from limb.
The more you think about it, the more you can see patterns all around you. Getting to work on time in the morning is the result of recognizing patterns in your daily commute and responding to changes in schedule and traffic. So here come the Google driverless cars, which are able to recognize all of these traffic and schedule changes faster than humans. Diagnosing an illness is the result of recognizing patterns in human behavior. And now that IBM Watson is getting into medical diagnosis, machines will do it better. The same goes for just about any field of expert endeavor - it's really just a matter of recognizing the right patterns faster than anyone else, and machines just have so much processing power these days it's easy to see them becoming the future doctors and lawyers of the world.
The future of intelligence is in making our patterns better, our heuristics stronger. In his article for Medium, Kevin Ashton refers to this as "selective attention" - focusing on what really matters so that poor selections are removed before they ever hit the conscious brain. While some – like Gary Marcus of The New Yorker or Colin McGinn in the New York Review of Books, may be skeptical of Kurzweil's Pattern Recognition Theory of Mind, they also have to grudgingly admit that Kurzweil is a genius. And, if all goes according to plan, Kurzweil really will be able to create a mind that goes beyond just recognizing a lot of words.
One thing is clear – being able to recognize patterns is what gave humans their evolutionary edge over animals. How we refine, shape and improve our pattern recognition is the key to how much longer we'll have the evolutionary edge over machines.
[image: Human intelligence with grunge texture / Shutterstock]
Northwell Health is using insights from website traffic to forecast COVID-19 hospitalizations two weeks in the future.
- The machine-learning algorithm works by analyzing the online behavior of visitors to the Northwell Health website and comparing that data to future COVID-19 hospitalizations.
- The tool, which uses anonymized data, has so far predicted hospitalizations with an accuracy rate of 80 percent.
- Machine-learning tools are helping health-care professionals worldwide better constrain and treat COVID-19.
The value of forecasting<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNTA0Njk2OC9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTYyMzM2NDQzOH0.rid9regiDaKczCCKBsu7wrHkNQ64Vz_XcOEZIzAhzgM/img.jpg?width=980" id="2bb93" class="rm-shortcode" data-rm-shortcode-id="31345afbdf2bd408fd3e9f31520c445a" data-rm-shortcode-name="rebelmouse-image" data-width="1546" data-height="1056" />
Northwell emergency departments use the dashboard to monitor in real time.
Credit: Northwell Health<p>One unique benefit of forecasting COVID-19 hospitalizations is that it allows health systems to better prepare, manage and allocate resources. For example, if the tool forecasted a surge in COVID-19 hospitalizations in two weeks, Northwell Health could begin:</p><ul><li>Making space for an influx of patients</li><li>Moving personal protective equipment to where it's most needed</li><li>Strategically allocating staff during the predicted surge</li><li>Increasing the number of tests offered to asymptomatic patients</li></ul><p>The health-care field is increasingly using machine learning. It's already helping doctors develop <a href="https://care.diabetesjournals.org/content/early/2020/06/09/dc19-1870" target="_blank">personalized care plans for diabetes patients</a>, improving cancer screening techniques, and enabling mental health professionals to better predict which patients are at <a href="https://healthitanalytics.com/news/ehr-data-fuels-accurate-predictive-analytics-for-suicide-risk" target="_blank" rel="noopener noreferrer">elevated risk of suicide</a>, to name a few applications.</p><p>Health systems around the world have already begun exploring how <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7315944/" target="_blank" rel="noopener noreferrer">machine learning can help battle the pandemic</a>, including better COVID-19 screening, diagnosis, contact tracing, and drug and vaccine development.</p><p>Cruzen said these kinds of tools represent a shift in how health systems can tackle a wide variety of problems.</p><p>"Health care has always used the past to predict the future, but not in this mathematical way," Cruzen said. "I think [Northwell Health's new predictive tool] really is a great first example of how we should be attacking a lot of things as we go forward."</p>
Making machine-learning tools openly accessible<p>Northwell Health has made its predictive tool <a href="https://github.com/northwell-health/covid-web-data-predictor" target="_blank">available for free</a> to any health system that wishes to utilize it.</p><p>"COVID is everybody's problem, and I think developing tools that can be used to help others is sort of why people go into health care," Dr. Cruzen said. "It was really consistent with our mission."</p><p>Open collaboration is something the world's governments and health systems should be striving for during the pandemic, said Michael Dowling, Northwell Health's president and CEO.</p><p>"Whenever you develop anything and somebody else gets it, they improve it and they continue to make it better," Dowling said. "As a country, we lack data. I believe very, very strongly that we should have been and should be now working with other countries, including China, including the European Union, including England and others to figure out how to develop a health surveillance system so you can anticipate way in advance when these things are going to occur."</p><p>In all, Northwell Health has treated more than 112,000 COVID patients. During the pandemic, Dowling said he's seen an outpouring of goodwill, collaboration, and sacrifice from the community and the tens of thousands of staff who work across Northwell.</p><p>"COVID has changed our perspective on everything—and not just those of us in health care, because it has disrupted everybody's life," Dowling said. "It has demonstrated the value of community, how we help one another."</p>
"You dream about these kinds of moments when you're a kid," said lead paleontologist David Schmidt.
- The triceratops skull was first discovered in 2019, but was excavated over the summer of 2020.
- It was discovered in the South Dakota Badlands, an area where the Triceratops roamed some 66 million years ago.
- Studying dinosaurs helps scientists better understand the evolution of all life on Earth.
Credit: David Schmidt / Westminster College<p style="margin-left: 20px;">"We had to be really careful," Schmidt told St. Louis Public Radio. "We couldn't disturb anything at all, because at that point, it was under law enforcement investigation. They were telling us, 'Don't even make footprints,' and I was thinking, 'How are we supposed to do that?'"</p><p>Another difficulty was the mammoth size of the skull: about 7 feet long and more than 3,000 pounds. (For context, the largest triceratops skull ever unearthed was about <a href="https://www.tandfonline.com/doi/abs/10.1080/02724634.2010.483632" target="_blank">8.2 feet long</a>.) The skull of Schmidt's dinosaur was likely a <em>Triceratops prorsus, </em>one of two species of triceratops that roamed what's now North America about 66 million years ago.</p>
Credit: David Schmidt / Westminster College<p>The triceratops was an herbivore, but it was also a favorite meal of the T<em>yrannosaurus rex</em>. That probably explains why the Dakotas contain many scattered triceratops bone fragments, and, less commonly, complete bones and skulls. In summer 2019, for example, a separate team on a dig in North Dakota made <a href="https://www.nytimes.com/2019/07/26/science/triceratops-skull-65-million-years-old.html" target="_blank">headlines</a> after unearthing a complete triceratops skull that measured five feet in length.</p><p>Michael Kjelland, a biology professor who participated in that excavation, said digging up the dinosaur was like completing a "multi-piece, 3-D jigsaw puzzle" that required "engineering that rivaled SpaceX," he jokingly told the <a href="https://www.nytimes.com/2019/07/26/science/triceratops-skull-65-million-years-old.html" target="_blank">New York Times</a>.</p>
Morrison Formation in Colorado
James St. John via Flickr
|Credit: Nobu Tamura/Wikimedia Commons|
Archaeologists discover a cave painting of a wild pig that is now the world's oldest dated work of representational art.
- Archaeologists find a cave painting of a wild pig that is at least 45,500 years old.
- The painting is the earliest known work of representational art.
- The discovery was made in a remote valley on the Indonesian island of Sulawesi.
Oldest Cave Art Found in Sulawesi<span style="display:block;position:relative;padding-top:56.25%;" class="rm-shortcode" data-rm-shortcode-id="a9734e306f0914bfdcbe79a1e317a7f0"><iframe type="lazy-iframe" data-runner-src="https://www.youtube.com/embed/b-wAYtBxn7E?rel=0" width="100%" height="auto" frameborder="0" scrolling="no" style="position:absolute;top:0;left:0;width:100%;height:100%;"></iframe></span>
The Persian polymath and philosopher of the Islamic Golden Age teaches us about self-awareness.