Thanks for the option

I just couldn’t help myself. What a motto! “Freeze your eggs, Free your career!”

You’ve probably heard that (by January, 2015) Facebook and Apple are offering to cover egg-freezing costs as part of their employee benefits. I have mixed feelings about this. (You’re like, “Welcome to the club, captain obvious.” I get it.)

Women, theoretically, have many options: Be a mom, don’t be a mom, be a mom early, be a mom late, be a stay-at-home mom, a part-time-work mom, a full-time-work mom. It’s an emotional minefield, driven by conscious and subconscious perceptions of what’s “expected” of you (from society, your own experience, your religion, what have you). It can be a double-edged blend of selfishness (what do “I” want) and selflessness (what’s “best” for the family/children).

I’m not going to deconstruct all that, because in my personal opinion, there’s no right answer. You’ve got to weigh all those factors, do what’s best for you, and do the best you can.

I do sincerely applaud Facebook and Apple for supporting some of the many choices a woman can make. But here’s the thing that sticks in my craw – why aren’t those options equally financially supported? Purposefully or accidentally, this decision deems that some of those options are ok but not all.

The overall point is to support and maintain an effective female workforce, right? So why doesn’t it work like this: $20k for your choice. Better yet – don’t force women to specify/justify that decision to their employer. Have this lump sum go into a flex account that can be used as they see fit:

  • Get $20k to freeze your eggs.
  • Get $20k for childcare**
  • Get $20k to spend money to save time (laundry services, housecleaning, chef, errands, etc)
  • Get $20k towards surrogacy, infertility treatments, or adoption expenses
  • Get $20k towards maternity leave (that is nearly always unpaid, by the way)
  • And – last but absolutely not least – get $20k even if you choose not to have children.

I know this is a sticky topic. I’m not saying there’s a right answer. I’m just saying that let’s not stop here. Apple/Facebook/Google/Microsoft, like it or not, are models that the rest of tech looks up to. In a time where flexibility attracts the best young talent, let’s support women making their own choices. Imagine the potential: What if you made yourself a company where the most talented women in the world were beating down the doors to join you?

** My sources tell me that this might cover a year – year and a half of childcare. If this allows a woman to keep her job after having a child, her earning potential would likely continue/increase over that time. That way, by the time the lump sum is used up, wouldn’t she in a better place to afford the childcare after that?

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Reblog: 10 ways Founders Sabotage Themselves

Simple Sabotage Field Manual

I can see why Jon Evans wrote this post for founders, but these are good lessons for anyone in a startup. A company’s culture is a reflection of the founder’s personality- the good and the bad. Someone wise once told me that if you’re one of the first 10 employees in a startup, you’re expected to save the company a few times. Avoiding one of these pitfalls might add a mark to your personal “savior” tally.

For me, Lessons #1 (“premature scaling is the root of all evil”) and #4 (“you are not a platform”) are my fatal flaws. I want to solve all the problems (all of the things!) as soon as I see them. Right now! A lesson I’m *trying* hard to learn is lazy instantiation. The problem is right there in the name – it’s lazy and I’m not. Enough said. But it’s better to start with something small and well-built (Lesson #2: “technical debt will kill you”) then try to build a beautiful, perfect product that will solve all your problems as version 1. That’s what life is – you never solve all your problems. You just haven’t stumbled across the new ones yet.

Speaking of lesson #2, “the best solution here is to scrap the whole thing and rewrite it from scratch” was one of the hardest and most valuable things I’ve ever learned. Lesson #10 (“how to report a bug”) is pure poetry – the words “it’s broken” are meaningless without the path (“when I did X, I expected Y, but got Z”). You don’t have to be an engineer to see why this makes sense.

Lesson #8 (“Stop managing by crisis”) was an interesting one for Jon to include. Managment is one of the most underestimated difficulties in any work environment, but the added pressure of a startup rachets even minor issues up a notch. I think you could have an entirely separate “top ten” of management mistakes. I’d probably be qualified to write that myself, since I’ve probably made every single one. I think most managers would say the same. But I digress – lesson #8 reminds you that crises are precious things. Having that adrenaline-based kick in the pants can get results, but you can’t hijack that for every problem that comes along. “Because nobody can work in crisis mode all the time, and after a few weeks it loses all meaning and begins to just breed resentment.” Well said.

That’s the end of my little TL;DR summary of this post – you should take 5 minutes and read the full thing. It might just save your company.

http://techcrunch.com/2014/06/07/dear-clients-please-stop-ten-ways-founders-sabotage-themselves/

7,420 miles

My trusty shoes

I have a lot of science-focused posts writing themselves in my brain (the push for open data, science you can do with a smartphone, the “should I join a startup” series…. they sound pretty interesting, right?) I haven’t had much time to put pen to paper these last few weeks. But in a lot of ways, this post is about science. You’ll see.

Not unlike a lot of other driven, hard-working, slightly Type-A people, I have a pretty intense hobby: Barefoot running.

I began running during my second year of grad school as a stress-management technique (thanks to some encouragement from a very dear friend). He actually baited me by promising a big slice of cheesecake when I was able to hit 4 miles, aka, the “campus drive loop” around Stanford. So really, this obsession began with cheesecake.

Initially, running was our excuse to get out of lab so we could actually enjoy some California weather. We were in the habit of getting in early and leaving late, so we were a bit vitamin-D deprived. There was a functional (albeit slightly scary) shower in our building, so I’d get in around 9 or 10 a.m., go out for a run around 4 or 5 p.m., take a quick shower, eat dinner, then go back work for another few hours. Rinse, repeat.

I started running in normal shoes (arch support, higher padded heel, etc). I enjoyed my little afternoon excursion, but I could never quite get past that 4-mile mark and got tired of “having” to buy new kicks every 6 months (the arch would fall, the padding would wear, etc). I found an old pair of track-style shoes in my closet (thin tread, no heel, no arch support, bonus: bright pink and blue 80s colors) and started running in those. It took a few weeks, but I remember for the first time getting that feeling of “I’m not ready to stop” when I saw my finish line. That, my friends, was the beginning of a deep dive into long distance running. And what a glorious ride it has been.

Around that time, I switched running partners for another grad school buddy of mine. We would hit the pavement and talk about our research, mostly venting frustrations about difficult minutia we were troubleshooting, the concerning habits of our labmates, and how little impact our work would have in the long term. It was as much physical therapy as it was mental. Though I didn’t realize it at the time, expressing opinions on my work in a “judgement-free zone” built the framework of my worldview on research. Its values, its pitfalls, and where and how I fit into its structure. Ultimately, this worldview led to me leave grad school to join a startup created by that same running buddy.

Those little track shoes got me through my first half-marathon. (As you can see, I kind of destroyed them).

Track shoes

A few months later, I switched into Vibrams (this would be about 2 and a half years ago now), and I’m still wearing the same pair. Those shoes took me so many places. I ran up mountains, to the ocean, through redwoods, around islands, in the desert, through wine country (multiple times), and even recently through some snow and ice (thanks, winter).

They helped me through a major life transition from grad school to a startup, a shoulder surgery, 80-hour workweeks, an almost-completed marathon training, and a full running-form rebuild when my marathon training failed. Then they saw me through a cross-country move and an intense job hunt. Now they are seeing me through my next professional step with a digital health startup.

I guess what I’m trying to say today (which happens to be International Barefoot Running Day) is that this hobby made me a better person and a better scientist. I hope you’ve got something that provides as much physical and mental benefit to you as well.

And with that (you guessed it) I’m going out for a run.

 

Don’t wait until 2057

A few months after I started graduate school, my mom saw this cartoon and mailed it to me. I’ve kept it on my desk ever since. It reminds me that I come from a long history of hard-working women and to keep showing people what I’m made of.

There’s a lot of news on Equal Pay Day in D.C. right now. Actually, there’s a lot of news about it everywhere – I can’t read the New York Times or turn on NPR without hearing about it. And that’s awesome.

It’s exciting that this is getting so much publicity, but progress is slow going. At the current rate, the pay gap won’t close until 2057 [1]. That means my/our future daughters will be in their 30’s and 40’s before they are paid equivalently to men.

While I fervently support federal initiatives to close the pay gap, it’s too… damn… slow. There are localized [2] and private sector efforts, but challenges exist there too. Think about it – getting a large corporation to make an effort for pay equality means it has to admit the problem exists in the first place (and possibly open itself up to discrimination suits).

I can’t passively wait for this problem to be fixed for me, and neither can you. I want to share three points that might change the way you think about the gender-based pay gap and (hopefully) encourage you to take a more active role in your financial future.

More women get graduate degrees than men [3]

Generally, the salary gap INCREASES as education increases [4]

(The salary gap for women with a master’s degree is larger than the gap for women with a bachelor’s degree. It regionally varies for women with a Ph.D. – in Boston, for example, the gap narrows at the Ph.D. level)

The salary gap INCREASES as age increases [1]

These points tell us a story. Although women are over 50% of the highly educated young workforce, their starting salary is lower and grows slower (compared to men) as time goes on. You cannot escape this trend with more education or more experience. Essentially, if you don’t start negotiating right away, you’ll never make up for that loss.

I know that graduate school induces “delayed adulthood” in many ways. We treat the first few years of grad school like college on repeat. We get married later in life, we have kids later in life, and enter the workforce later in life.

Outside of naiveté, I think we also don’t take our first salary as a serious negotiation because we haven’t experienced salary discrimination before. That’s a good thing – the typical graduate student’s salary is defined by the school and the department. No negotiation.

But on entering the “real world”, I felt (and still feel, truth be told) that not negotiating is ok at this stage – I tell myself I have plenty of time to work and I’ll make up for it as my skills increase. That feels good and gets me out of awkward professional conversations, but it’s blatantly untrue. You do NOT outgrow that gap.

So I have a few action items I’m going to put into practice. No “top ten” lists of how to negotiate, no pages and pages of research on pay inequality. Just a few simple to-do’s that I think can make a difference right now.

Action 1: Do your homework

In D.C., it’s common to not provide a salary range for a position, but instead ask the candidate what their “salary expectations” are. That’s empowering and incredibly uncomfortable.

So when I put down a salary range on an application, I try to do my homework. I use websites like GlassDoor.com to get an estimated salary for that position in my area (sometimes they even have an exact range for the company I’m applying to). I look up national averages in my field, correlate it to experience or education levels, and keep cost of living in mind.

Action 2: Negotiate your salary. Always. It’s expected.

Here’s the rub though – women can’t just negotiate like men. You’d think if you highlight your skills confidently then it should be obvious why you’re asking for more money. Wrong. Apparently that just makes you look like a jerk.

Sheryl Sandberg and Margaret A. Neale have some tips that are helpful (and somewhat depressing), like highlighting common interests and emphasizing larger goals, rather than focusing the conversation on you. You may have to evoke a communal female stereotype instead of just laying out facts.

Bonus points though – I think a woman knows how to read emotional expressions and adjust body language like a boss. So though there’s no simple instruction manual for negotiating, you can sense how the conversation is going and adjust your strategies accordingly. Just don’t lose ground.

Action 3: Document your work

Some companies do a good job with regular performance feedback. Most don’t. Be your own HR rep and document successful projects, important contributions, and when you go above and beyond to get something done. In a best-case scenario, you can use these examples when asking for a bonus. And if you find yourself in a worst-case scenario (where you have to provide evidence for a discrimination suit), you’ve got some paperwork to support you.

Action 4: Don’t forget about bonuses (or other non-salary perks)

On NPR yesterday morning, Sallie Krawcheck gave this illustrative example of bonus negotiation:

Sallie: So, we’ve got two employees. Let’s call them Joe and Joanne. And Joe and Joanne are both set to make $5 in bonus let’s say.

Now, Joe comes into my office and Joe says, hey Sallie, you know, I really I’ve had a great year, I’d like to make 10 this year. After Joe leaves, I call my head of HR, and we sort of say can you believe this? Joe wants to make 10, he’s in for five, ha, ha, ha.

Well, time goes by. It’s time to put those numbers on the piece of paper. And we start to look and we say, look, we don’t want to lose him. Let’s put him in for seven. Right? OK. So, we’ve done that. Now, what does Joanne make?

David Greene (NPR Host): She gets the five.

Sallie: Wrong. She gets three. Because the bonus pool doesn’t go up. Bonus pool is 10 – five and five. She didn’t ask for anything. So, they’re both in for five, he asks for 10; we give him seven.

I don’t know about you, but on hearing that I was stunned. Logically, of course the bonus pool doesn’t go up, but I didn’t consider that my bonus could actually be reduced if I don’t ask for a deserved increase.

Action 5: Don’t be complacent after your first negotiation

If you’ve negotiated a higher salary when you started your new job – bravo! If not, all is not lost. You should continue to negotiate in the future. Cost-of-living rarely goes down and you don’t get dumber with more experience. It’s as simple as that. So if you’ve worked hard and done a good job, ask for a reasonable increase. You know Joe is going to.

 

References:

[1] Jacqueline Berrien, Chair, U.S. Equal Employment Opportunity Commission*

[2] Boston Closing the Wage Gap: Becoming the Best City in America for Working Women

[3] U.S. Department of Education, National Center for Education Statistics (2013):

In 2011-2012:

    • 452,038 women graduated with master’s degrees, compared to only 302,191 men
    • 87,451 women graduated with doctoral degrees, compared to only 82,611 men.

[4] Victoria Budson, Founding Executive Director, Women and Public Policy Program, Harvard Kennedy School of Government*

 

* Quoted from the event “More Than a Number: Combatting Pay Discrimination in the Workplace” on April 7, 2014 at The Center for American Progress in Washington D.C.

The most frustrating (and least publicized) thing about science

Photo by David A. LaSpina, JapanDave.com

Photo by David A. LaSpina, JapanDave.com

A close friend suggested I read Zen and the Art of Motorcycle Maintenance. In a book about a cross-country journey, mental illness and self-discovery, I was surprised to find an exquisite description of the most common zemblanity of science.

For those of you who aren’t familiar with the term ‘zemblanity’, consider this your word-of-the-day:

“So what is the opposite of Serendip, a southern land of spice and warmth, lush greenery and hummingbirds, seawashed, sunbasted? Think of another world in the far north, barren, icebound, cold, a world of flint and stone. Call it Zembla. Ergo: zemblanity, the opposite of serendipity, the faculty of making unhappy, unlucky and expected discoveries by design. Serendipity and zemblanity: the twin poles of the axis around which we revolve.”

– Armadillo by William Boyd

The book Zen is a first-person narrative. The narrator begins describing the life of Phaedrus, a highly intelligent man who began college at the age of 15 studying biochemistry and molecular biology. We discover that Phaedrus is actually the narrator himself, before a severe mental break from reality and a subsequent electroconvulsive shock therapy treatment. This procedure so altered his personality and brain structure that Phaedrus is, in fact, an entirely separate person. Other than brief flashes of memory, the narrator discovers Phaedrus almost as you would discover any stranger – by what they leave behind. Thankfully for the narrator, Phaedrus was a prolific writer.

During his studies in college, Phaedrus began to think about the scientific method. This dogma instructs us to form a hypothesis, create experiment(s) to test said hypothesis, and then make an evaluation based on the experiments. If planned and executed correctly, the hypothesis should be proven true or false. In other words, you could say this series of steps is meant to scientifically determine truth.

But as Phaedrus continued his philosophical evaluation, focusing specifically on hypothesis generation, he realized something.

“As he was testing hypothesis number one by experimental method a flood of other hypotheses would come to mind, and as he was testing these, some more came to mind, and as he was testing these, still more came to mind until it became painfully evident that as he continued testing hypotheses and eliminating them or confirming them their number did not decrease. It actually increased as he went along.”

At first, this was an amusing thought. He coined the law: “The number of rational hypotheses that can explain any given phenomenon is infinite”. He even found it helpful during times of scientific frustration:

“Even when his experimental work seemed dead-end in every conceivable way, he knew that if he just sat down and muddled about it long enough, sure enough, another hypothesis would come along. And it always did.”

I think any scientist doing independent, discovery-based work can empathize with that situation. It’s the thing that keeps you going when you’ve hit your head against the same wall for weeks or months. It’s anti-boring. Science is discovery focused, and there’s always a new detail to uncover – no matter how small.

But if you think about this situation in another light – really think about it, as Phaedrus did – doesn’t this feel a bit… unproductive? You begin with a problem – a real, tangible problem that you are going to solve. After 6 months, or a year, or two years, you find yourself describing a particular nuance in so much detail that the original problem isn’t even mentioned.

You start with an elevator pitch that anyone could relate to, such as, “I’m going to determine why Cancer Type X responds to Therapeutic A, but Cancer Type Y does not.” But you end up describing something entirely different, like how the sensitivity setting of a particular instrument affects the determination of what’s-it in the whatchamacallit method.

Unfortunately, Phaedrus couldn’t reconcile his discovery with the purported purpose of science.

“If the purpose of the scientific method is to select from among a multitude of hypotheses, and if the number of hypotheses grows faster than the experimental method can handle, then it is clear that all hypotheses can never be tested. If all hypotheses cannot be tested, then the results of any experiment are inconclusive and the entire scientific method falls short of its goal of establishing proven knowledge.”

And this wasn’t the only thing that shook him. He realized that not only was the method itself flawed, the result of the method was also flawed. Instead of determining an unshakeable truth, what is considered “truth” or “fact” is simply the most superior analysis of the time. This was similarly paraphrased by Einstein as:

“Evolution has shown that at any given moment out of all conceivable constructions a single one has always proved itself absolutely superior to the rest.”

So truth was in fact dependent upon time.

“Some scientific truths seem to last for centuries, others for less than a year. Scientific truth was not dogma, good for eternity, but a temporal quantitative entity that could be studied like anything else.”

This is a bit surprising at first, but in a split second you realize that of course this is true. Our understanding of a situation is constantly updated with the presence of new knowledge. Phaedrus eventually determined that “the predicted results of scientific inquiry and the actual results of scientific inquiry are diametrically opposed here, and no one seems to pay too much attention to the fact.” Hence, this is the biggest zemblanity of science.

I’ll stop us here, rather than continue down the rabbit hole of Phaedrus’ analysis. Poor Phaedrus did not take this well. Believing now his effort in the sciences to be entirely futile, and science to be the major producer of multiple, indeterminate, and relative truths in the world, he simply quit. At the age of 17, he was expelled from the University for failing grades. After a series of other events, we eventually find Phaedrus back in an academic setting, but instead of studying science, he studies philosophy.

I think Phaedrus’ realizations resonate with me – and perhaps it resonates with you as well – because I believe I have been where Phaedrus is. I have realized how futile science can feel. How you feel like you are digging an increasingly faceted hole rather than a path forward. Most scientists are naturally analytical people, and may get into science because they are seeking a world where they can determine black and white truths. But instead, they are (sometimes harshly) confronted with the grayscale reality.

For most, this is simply a process of maturation. You adapt. I can distinctly remember when my worldview shifted into the gray and how it deeply impacted my personality and outlook. It was a watershed moment for me. But some can’t reconcile this realization, and instead find something else to do, like Phaedrus.

This raises an interesting question: If I agree with Phaedrus’ statements, which I do (mostly), why am I still a scientist? Why is anyone?

Ultimately, I think it’s a distinction in what you believe science produces. I believe science produces knowledge, not truth. Phaedrus eventually sought truth elsewhere, in philosophy. Though I enjoy philosophical whimsy now and then, I personally do not find truth in philosophy either. I find a dizzying spectacle of thought dissection (similar to the hypothesis conundrum described earlier) that leaves me with more questions than answers. But unlike with science, I don’t get the same satisfaction at the end of the process.

I think that’s a difference too – we’re all seeking answers in our work. You may or may not find them. But you find a situation where that process still fulfills you. I went through a “Phaedrus” moment when my science produced results I did not find valuable. I think this is another common situation, one that is often mistaken for an existential crisis (“I am not meant to be a scientist”) and leads to many talented thinkers, like our semi-fictional Phaedrus, to quit. Instead of quitting, I instead found a value-matched environment.

Since coming to terms with Phaedrus’ conclusions seems (to me) to be a common philosophical process for scientists, but also seems to be one of the least-advertised elements of the field, I think we should be more open about the realities of scientific work. Zen was published in 1974, but I thought his dissection of the scientific process is just as relevant today. This means we should be better educating scientists and non-scientists about how science actually works and what it produces at the end. We should value the process of knowledge building instead of just chasing the next PR headline.

I hope you’ve enjoyed this little philosophical foray. It certainly made me reflect on my evolution as a scientist. As a scientist, what other watershed moments have you experienced that aren’t advertised as part of the process?

Reblog: Why you shouldn’t decide anything important at your board meeting

This is a great post about how to prepare when getting a group to make a consensus. The official meeting shouldn’t be the first time you pose an important (potentially game-changing) question, especially one that you are heavily invested in. Though written specifically for entrepreneurs regarding board meetings, I think it’s good life advice. And for you scientists with the commonly-dreaded committee meeting coming up: I think it’s worth a read by you too.

Check it out here:

http://techcrunch.com/2014/03/19/why-you-shouldnt-decide-anything-important-at-your-board-meeting/

ScaaS: Science as a service (a research revolution)

CloudLab

Greetings, my fellow colleagues of tedium. “Give me your tired, your poor, your huddled masses yearning to breathe free …” Welcome to the new world of research: ScaaS.

It’s time to bring us out of the dark ages. No more old PCs running Windows 2000. No more equipment still using floppy drives. No more carpal tunnel syndrome from repetitive tasks a robot could (and SHOULD) do. It’s not just about our quality of life – it’s about changing the devastating trend of costly R&D.

I’m happy to say that the future is here – or, at least, we’re on the brink of it.  I’m calling it science as a service. It’s like software as a service, but for the research industry. Even if you aren’t familiar with the term SaaS, you’ve probably used an implementation of it. SaaS-based products host their software and your associated data on the cloud, and you interact with it via a simple web browser interface.

Now consider using that model for your favorite scientific experiment:

Instead of purchasing the hardware ($20k – $100k, or more), dealing with the software, maintaining the instrument, and doing the experiment by hand, you pop open your favorite web browser. You select the experiment and direct every detail by specifying a series of options (cell type, temperature, internal standard, instrument settings, etc. – if it can be altered, there should be an option for it). Maybe you also select parameters for how your data should be analyzed, how many times it should be repeated, or you select a desired completion date. Click, click, click, and your little experiment is on its way.* And you are on to bigger and better things.

Sounds great, right? Other people think so too. There are already a few names in this field. You should check out this great talk about how Emerald Therapeutics’ Symbolic Laboratory creates a construct for lean research (and how “lean research” could no longer be an oxymoron). TechCrunch blogged recently about Transcriptic and Benchling, and companies like Synthego, Gen9, and Gingko Bioworks are making headway too. 

So what’s keeping this from being immediately adopted in every lab in the country?

First, it’s probably because most experiments are not done in an automated fashion. If you go through a web interface to order an experiment, but then a human in a CRO does it for you, this doesn’t help much. It may save you some time, but it’s not a scalable or cost-efficient model. But just because these experiments aren’t normally done in an automated way doesn’t mean they can’t be done in an automated way. Most people still prefer grad students as a cheap form of labor (students making just over minimum wage, in fact), even though an automated instrument is more cost-effective in the long run.

But there’s another problem. It’s a mindset. Let’s face it: We scientists can be greedy. We just don’t want an experiment to be out of our hands. There is a biased attitude of trust that if you do it yourself, it’s “done right”. But the do-it-yourself model hasn’t worked out so well for us in terms of reproducibility. I’m not saying that you shouldn’t be concerned about handing over your experiments, but if a robot is doing the work you can take solace in that it will do what it’s programmed to do.** Robots don’t make complex errors like forgetting one element of a buffer recipe because it hasn’t had its morning coffee. The errors are standardized and if a major problem occurs, the robot stops entirely.

Lastly, scientists need to be more demanding about data. We need to make a priority of gathering it, storing it, and sharing it. In order to trust an experiment to a ScaaS system, the user needs to get back all the data (raw data, meta data, instrument files) they can get their hands on. Not just for the experiment at hand, but for the controls too. And not just the control run before their experiment – every control run. Ever. That should all be open-access. That way a user could investigate global changes in the behavior of the instrument, not just see an isolated period in time when their experiment was run. I would even suggest providing video records of the experiment in progress. (If you can afford a webcam to watch your kitty sleep all day, then a ScaaS center can afford them to watch their robots.) 

In conclusion – the solution is out there. But to adopt this system, we have to change the way we do science. We need to start integrating automation on all levels, incorporating computer science into the scientific way of life, and most of all, becoming gluttons for data.

* There is, of course, a potential problem with this. I’m not suggesting that only one powerhouse should dominate the market for a particular experiment. It would disastrous to find out later they’d done something wrong – remember the fiasco when we found out the major breast cancer cell line MDA-MB-435 was actually a melanoma cell line? I don’t think we need to get ourselves in that potential situation. I think competition within the private sector can do what it does best – have companies compete until a few “gold standard” options exist that are well-validated and trusted.

** The old adage “garbage in, garbage out” applies here. To properly program a robot, you have to have a unique blend of science and computer science savvy. I’m privileged to know some of these gurus, and in the right hands, this works.