David and Jen on their wedding day, and Jen undergoing treatment for her rare sarcoma.
It was 15 years ago this month, but I'll never forget those words. When my wife Jen and I asked her oncologist about our plans to start a family, he calmly replied, "Well, I wouldn't do so unless Dave is prepared to be a single father."
About 50 percent of all people with cancer have a rare type, like the one Jen was fighting.
Time stood still. The danger crystalized — we were in a battle for my beautiful bride's life, and the odds were not in our favor.
We felt every emotion expected. Anger, sadness, confusion, frustration, and especially fear. But we made a very intentional choice to take that fear, put it to the side, and do everything we could to live our lives together to the fullest.
We focused first on Jen's health and learned everything we could about MFH Sarcoma. I was with her every step of the way — for hundreds of medical appointments, six intense surgeries, and twenty different types of chemotherapy. During such a challenging time, our choice to reject fear allowed us to live our best lives. Our careers blossomed, we enjoyed several international vacations, and Jen inspired thousands of fellow patients through her blog and speeches.
When we researched treatment options we learned that Jen was not alone. About 50 percent of all people with cancer have a rare type, like the one Jen was fighting. However, rare cancers don't get the funding they desperately need so effective treatment options are hard to find. The lack of funding felt unfair — and urgent. We didn't worry about everything that can go wrong when starting a new venture. Instead, we jumped in head first and convinced a small group of friends and family to ride stationary bikes with us to raise money for rare cancer research.
Jen Goodman Linn, riding a stationary bike for Cycle for Survival.
(Courtesy David Linn)
From those humble beginnings, Cycle for Survival grew steadily. After starting from scratch, Jen and I ran Cycle for Survival on our own for two years. We quickly realized that if we wanted to help as many people as possible, we needed the best partners. In 2009, we agreed that Memorial Sloan Kettering Cancer Center would take over the ownership of Cycle for Survival and Equinox officially became the Founding Partner. Flash forward to today, and Cycle for Survival has raised more than $220 million! I'm proud that 100% of every donation, yes every penny, goes directly into life-saving rare cancer research within six months of the annual indoor cycling events, which now take place in 17 cities nationwide.
While Cycle for Survival's trajectory was heading straight up, Jen's health struggle was devastatingly swinging up and down. With her incredible spirit and tenacity, Jen would beat the cancer through chemo and surgery, but then it would frustratingly come back again and again. After going into remission six times, it returned with such a vengeance in 2011 that even the world's leading doctors were forced to say, "I'm sorry, there's nothing more we can do."
Those were the most difficult words I've ever heard, by far. I hope no other family has to hear these crushing words.
When Jen died soon after, I didn't know what would happen to me, to my life, and to Cycle for Survival. I do remember making two very important choices at the time. First, I chose to get out of bed and put one foot in front of the other. It wasn't easy. Tears, pain, and grief would hit at any hour of the day or night. I did have a great support network of family and friends who kept me moving forward. One friend in particular changed the route of her morning runs so that I would join her and start getting back to exercising.
My second key choice was to stay involved with Cycle for Survival. At times, it was an excruciatingly difficult decision because I felt the depth of my loss each and every time I stepped into one of the events. However, it was also rewarding and energizing because I could see firsthand how many people it was helping, even though it was too late for Jen.
I began to travel across the country with the Cycle for Survival staff. My hope was to spread the word about rare cancers; along the way I met a lot of wonderful people who shared their stories with me. What I soon realized is that each of us faces obstacles in our lives. For me, it was losing the person who I wanted to spend my life with. For others, it might be challenges with their kids or in their professional lives. The common theme is that we don't have control over the fact that we have to face these challenges. But the biggest lesson I've learned is that we very much do have a choice in how we react.
I made the choice to do everything I can to help rare cancer patients and their families and it has been transformative and healing for me. The small group who rode in the first Cycle for Survival event has grown into a powerful movement of nearly 40,000 riders making a real difference. If Jen were diagnosed today, there are new treatments available– including genomic sequencing, targeted therapies, and immunotherapies – that could help her. Those weren't even options a short time ago. That's the result of funding research.
A recent Cycle for Survival event shows the passion and power of the community.
(Courtesy David Linn)
I also want to share one more choice I made. Remember that friend who changed the route of her morning runs so I could start exercising after Jen died? Well, over the years friendship grew into love, and we're now building a home together and can't wait to see what the future holds for us.
So with all that in mind I ask – when you face those inevitable challenges in your life, how will you choose to react? Remember that even in the midst of hopelessness, you can find choices. Those will be the decisions that define and guide you.
Giving robots self-awareness as they move through space - and maybe even providing them with gene-like methods for storing rules of behavior - could be important steps toward creating more intelligent machines.
Now roboticists are going a step further, training their creations to do even better: understand their own image in space and interact with humans like humans do. Today, there are already robot-teachers like KeeKo, robot-pets like Moffin, robot-babysitters like iPal, and robotic companions for the elderly like Pepper.
But even these reasonably intelligent creations still have huge limitations, some scientists think. “There are niche applications for the current generations of robots,” says professor Anthony Zador at Cold Spring Harbor Laboratory—but they are not “generalists” who can do varied tasks all on their own, as they mostly lack the abilities to improvise, make decisions based on a multitude of facts or emotions, and adjust to rapidly changing circumstances. “We don’t have general purpose robots that can interact with the world. We’re ages away from that.”
Robotic spatial self-awareness – the achievement by the team at Columbia – is an important step toward creating more intelligent machines. Hod Lipson, professor of mechanical engineering who runs the Columbia lab, says that future robots will need this ability to assist humans better. Knowing how you look and where in space your parts are, decreases the need for human oversight. It also helps the robot to detect and compensate for damage and keep up with its own wear-and-tear. And it allows robots to realize when something is wrong with them or their parts. “We want our robots to learn and continue to grow their minds and bodies on their own,” Chen says. That’s what Zador wants too—and on a much grander level. “I want a robot who can drive my car, take my dog for a walk and have a conversation with me.”
Columbia scientists have trained a robot to become aware of its own "body," so it can map the right path to touch a ball without running into an obstacle, in this case a square.
Jane Nisselson and Yinuo Qin/ Columbia Engineering
Today’s technological advances are making some of these leaps of progress possible. One of them is the so-called Deep Learning—a method that trains artificial intelligence systems to learn and use information similar to how humans do it. Described as a machine learning method based on neural network architectures with multiple layers of processing units, Deep Learning has been used to successfully teach machines to recognize images, understand speech and even write text.
Trained by Google, one of these language machine learning geniuses, BERT, can finish sentences. Another one called GPT3, designed by San Francisco-based company OpenAI, can write little stories. Yet, both of them still make funny mistakes in their linguistic exercises that even a child wouldn’t. According to a paper published by Stanford’s Center for Research on Foundational Models, BERT seems to not understand the word “not.” When asked to fill in the word after “A robin is a __” it correctly answers “bird.” But try inserting the word “not” into that sentence (“A robin is not a __”) and BERT still completes it the same way. Similarly, in one of its stories, GPT3 wrote that if you mix a spoonful of grape juice into your cranberry juice and drink the concoction, you die. It seems that robots, and artificial intelligence systems in general, are still missing some rudimentary facts of life that humans and animals grasp naturally and effortlessly.
How does one give robots a genome? Zador has an idea. We can’t really equip machines with real biological nucleotide-based genes, but we can mimic the neuronal blueprint those genes create.
It's not exactly the robots’ fault. Compared to humans, and all other organisms that have been around for thousands or millions of years, robots are very new. They are missing out on eons of evolutionary data-building. Animals and humans are born with the ability to do certain things because they are pre-wired in them. Flies know how to fly, fish knows how to swim, cats know how to meow, and babies know how to cry. Yet, flies don’t really learn to fly, fish doesn’t learn to swim, cats don’t learn to meow, and babies don’t learn to cry—they are born able to execute such behaviors because they’re preprogrammed to do so. All that happens thanks to the millions of years of evolutions wired into their respective genomes, which give rise to the brain’s neural networks responsible for these behaviors. Robots are the newbies, missing out on that trove of information, Zador argues.
A neuroscience professor who studies how brain circuitry generates various behaviors, Zador has a different approach to developing the robotic mind. Until their creators figure out a way to imbue the bots with that information, robots will remain quite limited in their abilities. Each model will only be able to do certain things it was programmed to do, but it will never go above and beyond its original code. So Zador argues that we have to start giving robots a genome.
How does one do that? Zador has an idea. We can’t really equip machines with real biological nucleotide-based genes, but we can mimic the neuronal blueprint those genes create. Genomes lay out rules for brain development. Specifically, the genome encodes blueprints for wiring up our nervous system—the details of which neurons are connected, the strength of those connections and other specs that will later hold the information learned throughout life. “Our genomes serve as blueprints for building our nervous system and these blueprints give rise to a human brain, which contains about 100 billion neurons,” Zador says.
If you think what a genome is, he explains, it is essentially a very compact and compressed form of information storage. Conceptually, genomes are similar to CliffsNotes and other study guides. When students read these short summaries, they know about what happened in a book, without actually reading that book. And that’s how we should be designing the next generation of robots if we ever want them to act like humans, Zador says. “We should give them a set of behavioral CliffsNotes, which they can then unwrap into brain-like structures.” Robots that have such brain-like structures will acquire a set of basic rules to generate basic behaviors and use them to learn more complex ones.
Currently Zador is in the process of developing algorithms that function like simple rules that generate such behaviors. “My algorithms would write these CliffsNotes, outlining how to solve a particular problem,” he explains. “And then, the neural networks will use these CliffsNotes to figure out which ones are useful and use them in their behaviors.” That’s how all living beings operate. They use the pre-programmed info from their genetics to adapt to their changing environments and learn what’s necessary to survive and thrive in these settings.
For example, a robot’s neural network could draw from CliffsNotes with “genetic” instructions for how to be aware of its own body or learn to adjust its movements. And other, different sets of CliffsNotes may imbue it with the basics of physical safety or the fundamentals of speech.
At the moment, Zador is working on algorithms that are trying to mimic neuronal blueprints for very simple organisms—such as earthworms, which have only 302 neurons and about 7000 synapses compared to the millions we have. That’s how evolution worked, too—expanding the brains from simple creatures to more complex to the Homo Sapiens. But if it took millions of years to arrive at modern humans, how long would it take scientists to forge a robot with human intelligence? That’s a billion-dollar question. Yet, Zador is optimistic. “My hypotheses is that if you can build simple organisms that can interact with the world, then the higher level functions will not be nearly as challenging as they currently are.”
