Flick a switch on a dark winter day and your office is flooded with bright light, one of many everyday miracles to which we are all usually oblivious.
A physicist would probably describe what is happening in terms of the particle nature of light. An atom or molecule in the fluorescent tube that is in an excited state spontaneously decays to a lower energy state, releasing a particle called a photon. When the photon enters your eye, something similar happens but in reverse. The photon is absorbed by a molecule in the retina and its energy kicks that molecule into an excited state.
Light is both a particle and a wave, and this duality is fundamental to the physics that rule the Lilliputian world of atoms and molecules. Yet it would seem that in this case the wave nature of light can be safely ignored.
We are testing a combination of compounds which clear out dysfunctional cells in the body, called Senolytics, to see if we can extend maximum lifespan and healthspan in mice. Please subscribe, share, and fund our Lifespan.io campaign today!
If you’ve ever seen a “recommended item” on eBay or Amazon that was just what you were looking for (or maybe didn’t know you were looking for), it’s likely the suggestion was powered by a recommendation engine. In a recent interview, Co-founder of machine learning startup Delvv, Inc., Raefer Gabriel, said these applications for recommendation engines and collaborative filtering algorithms are just the beginning of a powerful and broad-reaching technology.
Raefer Gabriel, Delvv, Inc.
Gabriel noted that content discovery on services like Netflix, Pandora, and Spotify are most familiar to people because of the way they seem to “speak” to one’s preferences in movies, games, and music. Their relatively narrow focus of entertainment is a common thread that has made them successful as constrained domains. The challenge lies in developing recommendation engines for unbounded domains, like the internet, where there is more or less unlimited information.
“Some of the more unbounded domains, like web content, have struggled a little bit more to make good use of the technology that’s out there. Because there is so much unbounded information, it is hard to represent well, and to match well with other kinds of things people are considering,” Gabriel said. “Most of the collaborative filtering algorithms are built around some kind of matrix factorization technique and they definitely tend to work better if you bound the domain.”
Of all the recommendation engines and collaborative filters on the web, Gabriel cites Amazon as the most ambitious. The eCommerce giant utilizes a number of strategies to make item-to-item recommendations, complementary purchases, user preferences, and more. The key to developing those recommendations is more about the value of the data that Amazon is able to feed into the algorithm initially, hence reaching a critical mass of data on user preferences, which makes it much easier to create recommendations for new users.
“In order to handle those fresh users coming into the system, you need to have some way of modeling what their interest may be based on that first click that you’re able to extract out of them,” Gabriel said. “I think that intersection point between data warehousing and machine learning problems is actually a pretty critical intersection point, because machine learning doesn’t do much without data. So, you definitely need good systems to collect the data, good systems to manage the flow of data, and then good systems to apply models that you’ve built.”
Beyond consumer-oriented uses, Gabriel has seen recommendation engines and collaborative filter systems used in a narrow scope for medical applications and in manufacturing. In healthcare for example, he cited recommendations based on treatment preferences, doctor specialties, and other relevant decision-based suggestions; however, anything you can transform into a “model of relationships between items and item preferences” can map directly onto some form of recommendation engine or collaborative filter.
One of the most important elements that has driven the development of recommendation engines and collaborative filtering algorithms is the Netflix Prize, Gabriel said. The competition, which offered a $1 million prize to anyone who could design an algorithm to improve upon the proprietary Netflix’s recommendation engine, allowed entrants to use pieces of the company’s own user data to develop a better algorithm. The competition spurred a great deal of interest in the potential applications of collaborative filtering and recommendation engines, he said.
In addition, relative ease of access to an abundant amount of cheap memory is another driving force behind the development of recommendation engines. An eCommerce company like Amazon with millions of items needs plenty of memory to store millions of different of pieces of item and correlation data while also storing user data in potentially large blocks.
“You have to think about a lot of matrix data in memory. And it’s a matrix, because you’re looking at relationships between items and other items and, obviously, the problems that get interesting are ones where you have lots and lots of different items,” Gabriel said. “All of the fitting and the data storage does need quite a bit of memory to work with. Cheap and plentiful memory has been very helpful in the development of these things at the commercial scale.”
Looking forward, Gabriel sees recommendation engines and collaborative filtering systems evolving more toward predictive analytics and getting a handle on the unbounded domain of the internet. While those efforts may ultimately be driven by the Google Now platform, he foresees a time when recommendation-driven data will merge with search data to provide search results before you even search for them.
“I think there will be a lot more going on at that intersection between the search and recommendation space over the next couple years. It’s sort of inevitable,” Gabriel said. “You can look ahead to what someone is going to be searching for next, and you can certainly help refine and tune into the right information with less effort.”
While “mind-reading” search engines may still seem a bit like science fiction at present, the capabilities are evolving at a rapid pace, with predictive analytics at the bow.
It’s absolutely insane to go ahead with the summer Olympics in light of this horrid mess. It’s unlikely to end us. but it could hurt us all, badly. No disease of this kind could ask for a better opportunity to spread around the world than that which the Olympics are about to give it. It’s insane.
Probably not, but pathogens that damage brains may earn a special place in cosmic hell.
You are really starting to see the shape of the Singularity, ever more clearly, in the convergence of so many engineering and scientific discoveries, inventions, and philosophical musings.
I can say, without a doubt, that we are all living in truly extraordinary times!
This five-fingered robot hand developed by University of Washington computer science and engineering researchers can learn how to perform dexterous manipulation — like spinning a tube full of coffee beans — on its own, rather than having humans program its actions. (credit: University of Washington)
MAY 12, 2016, WASHINGTON (Army News Service) – “This is the most advanced arm in the world. This one can do anything your natural arm can do, with the exception of the Vulcan V,” said Johnny Matheny, using his right hand to mimic the hand greeting made famous by Star Trek’s Leonard Nimoy. “But unless I meet a Vulcan, I won’t need it.”
Matheny was at the Pentagon, May 11, 2016, as part of “DARPA Demo Day,” to show military personnel the robotic arm he sometimes wears as part of research funded by the Defense Advanced Research Projects Agency. DARPA is an agency of the U.S. Department of Defense responsible for the development of emerging technologies for use by the military.
When I look at technology and other things; my brain just dissolves all boundaries/ scope of the technology was originally defined for. For me, this is and has always been in my own DNA since I was a toddler. When I first looked at VR/ AR, my future state vision just exploded immediately where and how this technology could be used, how it could transform industries and daily lives, and other future technologies. So, I am glad to see folks apply AR and VR in so many ways that will prove valuable to users, companies, and consumers.
NVIDIA is working with various companies in different sectors such as automotive, manufacturing, and medical to bring AR benefits in their business. It is working with Audi, General Motors (GM), and Ford (F) to create a VR application where the consumer can design a car by changing its wheels, paint, or seat leather. NVIDIA is also working with European (IEV) furniture manufacturer IKEA to build a virtual reality application that allows the user to design their own rooms and homes.
The Wyss Institute at Harvard is creating miniaturised versions of human organs that could one day be used to test drugs as specific as the patients that take them.
