First of all, the Orville, Season 3 which is just wrapping up has been fantastic. If you at all like SciFi and especially Original or Next Gen Star Trek, this is a must watch.

Brilliant homage, but also stands so well on it's own. Engaging stories, fabulous characters, often thought provoking, and just a good time.

I know people have mixed opinions about Star Trek Discovery and I'm not looking to re-open the debate about the series generally, but this season's second last episode "Species Ten-C" as a singular episode is one of the best in all of Star Trek (all shows) that I've seen.

I also really liked Moon Knight as a Marvel show. Cool atmosphere, amusing plot, very compelling characters and fun.

Ms Marvel was good too, but Moon Knight was better.

I am not surprised at all.

Background: I have spent my whole career in the applications of AI/ML/Data Science to healthcare and the life sciences. In diagnostics, devices, drug development.

Disclaimer, some of this going to be repetitive from my own comment history, such as from back in June when there was a story about Apple struggling in Healthcare:
https://slashdot.org/comments....

The folks at Watson never really understood healthcare. They figured hey we did a great job with Jeopardy, that was AI, now we can do AI in healthcare. But first of all, Jeopardy was a big NLP knowledge graph. Sure that is AI, but the AI for healthcare applications is much more. And even then, you can not just throw âoeAIâ at healthcare, you need to understand all the different subproblems underneath.

Are you talking about drug discovery, medical claims and costs, diagnostics, treatment, devices, population health, genetics, providers, hospital networks, insurers, pharma, etc? What type of data are you dealing with: genetic, omics, imaging (digital pathology, radiology, mass spec, etc), RWE/claims, devices/streaming, behavioral, population health, chemical/molecular/drug entity, etc? And much more. Each has its own nuances, issues, problems, algorithms, approaches.

Slashdot user tomhath previously said it was a

but not more than that. User technomom has said

https://slashdot.org/comments....
https://slashdot.org/comments....

Over the course of my career, I have had multiple different interactions with the folks at Watson Health. It is a huge org, I am sure some people there got it, but I never met anyone who really did. I visited the fancy showcase/partnership development center they have in Boston with all the fancy digital and AR displays.

Looked AWESOME. Lots of money sunk in to impress the hell out of how shiny and cool everything is. It is IBM after all. But a very Potemkin construct.

Once you actually start talking to the people about what they had, how it was developed, how it was validated, why could it be trusted, what circumstances was it strong/weak in, future roadmap, etc. Anything about the real meat and bones. The conversation collapsed. Non technical talking points were regurgitated (we are IBM, this is cool, we know AI, we are working with MD Andersen). It was obvious that the REAL issues of AI in healthcare were not dealt with, it was all quick and dirty pilots, proof of concepts, things slapped together.

At conferences and various industry fora (in the pre-Covid times) I would meet lots of people working in Watson Health. They either did not last long there, or very flashy visionaries talking about broad generalities. There did not seem to be anyone there who could actually get stuff done on the ground. If they were, they were very frustrated I am sure. IBM threw money, charlatans who make careers off big ideas worked there for a bit, but in reality nothing bore fruit. Where were all the real doers?

And you know what, healthcare is HARD. Complex. You would never set things up this way from scratch, but there are reasons why things are the way they are. Scaling things up and dealing with humans, messy, sick, biology with all itâ(TM)s various complications and issues. The recent experiences with Covid should just highlight how messy it all is.

People will do one proof of concept or pilot. In one sub sub sub disease, on a small dataset, from a particular set of dates. Well medical practice changes, is that dataset from 3 years ago reflective of medicine today? That dataset was for men 20-40, does it work for women over the age of 50? It had samples from the Mayo clinic, is that how samples and patients are treated at the ER in Podunk, Middle of Nowhere? OK it worked for that one disease, how about for the other 5000? And on and on.

IBM has struggled in its main tech areas. And even people who do much better in tech (Google, Apple) are flopping in healthcare.

Good rule of thumb:
1. It is more complicated than you think
2. If you think it is very complicated, you are wrong. It is much, much worse.

I could keep going, but this is already a pretty long post. I guess the TLDR is that IBM thought we have a great âoeAIâ brand, letâ(TM)s throw money at healthcare. And never really cared to figure out healthcare and do it right.

BinaxNOW by Abbott is the most common (and only?) at home Covid test approved in the US.

Stupidly, this summer when they thought COVID was declining, they destroyed millions of kits. Or to be more clear, they weren't assembled yet, but all the components which would have made millions of kits.

Then they laid off the workers, and shut that whole product line.

Not too long afterwards, guess what? Oops, Delta variant is spiking.

Can't get it all up and running fast enough. Nationwide shortage.

Stupid, short sighted, short term corporate thinking.

https://www.fiercebiotech.com/...

https://www.nytimes.com/2021/0...

(Disclaimer: I have discussed this on Slashdot before as well, so some of this is repetitive from my own comment history).

I have spent my whole career in healthcare and the life sciences, but in a technical capacity. My background is in AI/Machine Learning/big data/etc., and I have worked in diagnostics, devices, drug development.

It has been really interesting and cool. The field is ripe with opportunities, and fascinating intellectual challenges.

But guess what, it is also very very complex, for a gajillion reasons. First of all, biology is hard. And people who are experts in this area are routinely surprised and wrong. Then the actual system of delivering healthcare has a lot of issues. Then you have regulatory things to consider, and laws like HIPAA.

Despite what people think, very few people are out there to screw others over. Sure people like to make money like in any industry, and there are bad actors (FU Martin Shkreli), but they are a very small fraction of all the people who work here. There are underlying reasons and systemic trends which have led us to where we are, and no one would have designed things this way from scratch, but also does not mean that it was all put together this way to screw over people. One common phrase I have heard from people throughout my career is that we are all patients; those who work in this area also get sick, suffer from diseases and need care throughout their lives.

It has been funny and amusing though how people not in this area do not get it. Especially those coming from a tech background who have disrupted other areas and now are going to disrupt this field. They think they are smart and can solve all the problems.
https://xkcd.com/1831/ [xkcd.com]

Things just do not translate. Take my field of ML/AI. Algorithms, models in other domains do not need the same level of rigor. Train, and test and repeat. Deploy a model to show ads. It kinda works, that is OK. Do not have to submit to the FDA. Do not have to be careful with small amounts of data (you know how rare it is to get patient level data, especially from bio-specimens like blood, tissue, etc, and then have longitudinal follow up with it).

People may have initial POC wins, Googles recent work with breast cancer as an example. But scaling it, working with real world issues, variability, etc, things just fall apart.

Googles Verily venture has been around for a long time, and nothing real has come out of it.

IBM Watsons failure in healthcare is well known and documented.

Haven (joint Amazon, Chase, Berkshire Hathaway venture) has failed.

I have seen it from the other end. Doctors, biologists who do not get tech think oh Google and Facebook are so big and smart, so if they work in this area, they will succeed. Big tech thinks oh we have done well in other fields, we can win this one too.

As someone who straddles both, I shake my head so many times. I constantly have recruiters reaching out. 90% of the time, I pass on any opportunity. The people involved just do not get it. I used to have FOMO, but after years, when I follow back up on things I declined, I usually was right. Only thing I have some regrets about is Flatiron, but lets see if Roche can really make that work. And even there, it was the right decision for me, their ML/AI stuff is pretty weak, their value was just in their datasets. And I think it was overinflated value, but that is a different post.

Anyway, the TLDR version is that Big Tech and people who are successful in other domains have a lot of hubris that we kicked butt here, we can do so there. And pretty much every time they fail in healthcare. And vice a versa, people in healthcare who do not get tech also fail when they try to do tech stuff. There is a small subgroup of capable folks who manage to succeed in the middle.

As has been commented in previous Slashdot stories:
Biology is not rocket science, it is harder.

Good rule of thumb:
1. It is more complicated than you think
2. If you think it is very complicated, you are wrong. It is much, much worse.

Citations:
https://slashdot.org/comments....
https://slashdot.org/comments....

Finally, I have worked in companies and have had colleagues/friends in companies that have tried to collaborate with Apple in Healthcare. OMG, the hubris has been amazing. We are Apple, the 2000 pound Gorilla. You need us, we do not need you. Everything will be how we want it to be.

In one former employer, we went from the Apple collaboration team being the place everyone wanted to be, other teams being raided for their highest performers, to a few years later that team having extremely low morale, frustrated, and seeing high turnover. For the sake of working with Apple, that company not only failed in that specific project, but gutted other endeavors of their best people, with not much to show for it.

I've said this before (including on /.) and will say it again, in the naive hopes that somehow these ideas will get to policy makers.

A lot of people are against the H1B system, and rightfully so given how badly it's been abused to harm US workers. Also to harm the H1B folks. So it results in damage to the field overall by bringing in cheap body shop labor, treating them as pseudo indentured servants, while harming career prospects and wage growth for US folks, and hurting the employee base overall.

On the other hand, there really is a valid problem that the H1B program was initially trying to solve, a shortage of qualified candidates for some roles (which are well paid and compensated).

I've hired H1Bs throughout my career, including now, for some roles. Not as a blanket hiring policy, but for when we couldn't find candidates. They were hired in direct competition against all comers, and were truly the best candidates for us (not just due to technical background, but previous experiences, "social fit" etc). They are by far not the cheapest paid, and fall in the middle to high end of our distribution. The preference actually was to hire domestic candidates if we could find them. Communication and common cultural touchstones are easier, as well as less bureaucratic overhead. This is what I think the original purpose of the H1B program was.

The current bureaucratic H1B process is a kafkaesque nightmare. DHS folks are really not qualified to judge jobs and backgrounds. As an example: Why is a PhD in stats or physics working as a "Machine Learning Scientist." That is not an appropriate degree for the role. Really?

The H1B process has been wholly abused. But there was a legitimate and enduring need that it had tried to solve.

The two quickest and easiest ways I've heard to solve this (which are not part of the current proposed "solutions") are:
1) Run the H1B process as an auction not a lottery. The auction number/bid is the salary you will offer the candidate. You want a foreigner, pay for them. If their background, experience, etc are something you truly need, your dollars will speak for themselves. Will significantly mitigate the cheap body shop problem. Toy example: only 10 people allowed, H1B visas will be issued to the 10 folks who will be paid the highest salary (with some safeguards like that salary can't be reduced by more than 5% in total for the full H1B duration).
2) Allow more mobility so that H1B workers are not so explicitly tied to an employer which lends itself to abuse.

Finally, especially on /. we get stuck on H1Bs, but Trump had stopped all illegal immigration, including family based, refugee, etc. There is more to legal immigration than just employment based.

Look,
We all know this is the new digital version of pump and dump driven by r/WSB.

There is nothing in GME's business that justifies these valuations.

The interesting thing though is that in the meantime, as long as the ride keeps running before you stop and are left holding the bag, there's opportunities to make money.

After writing off WSB, I realized that in the short term, one could make serious money. Now it's pretty much gambling. And I just put in a little bit of play money, not betting any core investments. But I've ridden highs on GME, BB and others. Just when I think it can't go up anymore, it does! I've popped in and out so often.

I've kept it limited, I know this is just crazy unsustainable. But on the other hand, why let all the other folks make the profit? Just have to time it.... famous last words.

I wonder when it will pop, and if I can keep ahead of it for long enough that it'll be a nice amount.

I have spent my whole career in healthcare and the life sciences, but in a technical capacity. My background is in AI/Machine Learning/big data/etc., and I have worked in diagnostics, devices, drug development.

It has been really interesting and cool. The field is ripe with opportunities, and fascinating intellectual challenges.

But guess what, it is also very very complex, for a gajillion reasons. First of all, biology is hard. And people who are experts in this area are routinely surprised and wrong. Then the actual system of delivering healthcare has a lot of issues. Then you have regulatory things to consider, and laws like HIPAA.

Despite what people think, very few people are out there to screw others over. Sure people like to make money like in any industry, and there are bad actors (FU Martin Shkreli), but they are a very small fraction of all the people who work here. There are underlying reasons and systemic trends which have led us to where we are, and no one would have designed things this way from scratch, but also does not mean that it was all put together this way to screw over people. One common phrase I have heard from people throughout my career is that we are all patients; those who work in this area also get sick, suffer from diseases and need care throughout their lives.

It has been funny and amusing though how people not in this area do not get it. Especially those coming from a tech background who have disrupted other areas and now are going to disrupt this field.

Things just do not translate. Take my field of ML/AI. Algorithms, models in other domains do not need the same level of rigor. Train, and test and repeat. Deploy a model to show ads. It kinda works, that is OK. Do not have to submit to the FDA. Do not have to be careful with small amounts of data (you know how rare it is to get patient level data, especially from bio-specimens like blood, tissue, etc, and then have longitudinal follow up with it).

People may have initial POC wins, Googles recent work with breast cancer as an example. But scaling it, working with real world issues, variability, etc, things just fall apart.

Googles Verily venture has been around for a long time, and nothing real has come out of it.

IBM Watsons failure in healthcare is well known and documented.

Now Haven has failed.

I have seen it from the other end. Doctors, biologists who do not get tech think oh Google and Facebook are so big and smart, so if they work in this area, they will succeed. Big tech thinks oh we have done well in other fields, we can win this one too.

As someone who straddles both, I shake my head so many times. I constantly have recruiters reaching out. 90% of the time, I pass on any opportunity. The people involved just do not get it. I used to have FOMO, but after years, when I follow back up on things I declined, I usually was right. Only thing I have some regrets about is Flatiron, but lets see if Roche can really make that work. And even there, it was the right decision for me, their ML/AI stuff is pretty weak, their value was just in their datasets. And I think it was overinflated value, but that is a different post.

Anyway, the TLDR version is that Big Tech and people who are successful in other domains have a lot of hubris that we kicked butt here, we can do so there. And pretty much every time they fail in healthcare. And vice a versa, people in healthcare who do not get tech also fail when they try to do tech stuff. There is a small subgroup of capable folks who manage to succeed in the middle.

I've always been amazed of how "ads" are the key fuel for silicon valley (Google, Facebook, many others have "ads" as their revenue source) when I've never thought they're that effective.

Don't get me wrong, ads have some value. Awareness of product, brand re-enforcement, etc. But IMO, the worth is nowhere near the amounts of money sloshing around in this eco system.

I'm glad people are actually testing this. For too long, it has seemed like this could not be questioned.

It will impact a lot of big companies and industries though. Watch out for a HUGE discrediting campaign.

Bitcoin isn't very easy to transact, at least from what I know. A few years ago I bought some crypto currencies. After struggling to figure out how, I ended up going to BitStampm, an Eastern Europe based exchange. Most of the exchanges were offshore.

One really needs an easy way to buy bitcoins, through simple electronic transactions. Should be as easy as buying a stock or ETF in a brokerage account.

How do people actually buy and hold bitcoin in a way that a grandmother could do it?

So to clarify, all the money goes to the employee.

Your auction bid is the employee's base salary. If there are x number of H1Bs, then the X highest proposed salaries would get H1Bs. So basically, only the employees that employers are actually willing to pay top dollar for would get H1bs, presumably because they really need them.

Now definitely some room for abuse here which they'd have to keep on top of, but much less relative to now.

Wanted to clarify, as I think you misunderstood the auction concept. I think you just understood it as a glorified "bribe", whoever pays some government agency the most gets their H1bs approved.

So I know that a lot of people on Slashdot are generally against the H1B system, rightfully so given how badly it's been abused.

I did want to share a perspective though that there really was an initial problem the H1B system was trying to solve, a shortage of qualified candidates for some roles (which are well paid and compensated).

We have a couple of H1Bs. They were hired in direct competition against all comers, and were truly the best candidates for us (not just due to technical background, but previous experiences, "social fit" etc). They are by far not the cheapest paid, and fall in the middle to high end of our distribution. The preference actually was to hire domestic candidates if we could find them. This is what I think the original purpose of the H1B program was.

The current bureaucratic process is a kafkaesque nightmare. DHS folks are really not qualified to judge jobs and backgrounds. As an example: Why is a PhD in stats or physics working as a "Machine Learning Scientist." That is not an appropriate degree for the role. Really?

The H1B process has been wholly abused. But there was a legitimate and enduring need that it had tried to solve.

The two quickest and easiest ways I've heard to solve this (which are not part of the current proposed "solutions") are:
1) Run the H1B process as an auction not a lottery. You want a foreigner, pay for them. If their background, experience, etc are something you truly need, your dollars will speak for themselves. Will significantly mitigate the cheap body shop problem.
2) Allow more mobility so that H1B workers are not so explicitly tied to an employer which lends itself to abuse.

Rome wasn't built in a day, and neither did Amazon get to where it is now in a few years. It was a long haul, and one of continuous re-investment in the business. How many times did we hear that Amazon is profitable, but puts all profit back into growth, so the net financials show losses?

That doesn't work well with Google's MO. Outside of a few areas like core search and Gmail, Google has been unable to stick with something and invest in it to enable that growth and market dominance. The list of abandoned Google projects which never got out of "beta" is a mile long.

You can never compete meaningfully that way, as Google's own multi-year fits and starts manifest here.

Look, that is a logical fallacy, a strawman argument.

There is room in the civil political discourse to discuss the right amount of taxation, and what those services pay for, or do not, and where we as a society want to draw the line. There is room to discuss the level of immigration a country finds useful and sustainable, anywhere from open borders to nobody gets in. There is room to discuss the level of legal consequences for any illegal action, including illegal immigration, or others.

These are all valid and important things for a country and government to figure out. Sure people will have, often passionate, opinions, and debates will get heated, but we have to accept that in free society. Itâ(TM)s the price of democracy.

For the most part, I do not think that is what people are talking about when they talk about social media policing content.

Instead, I would categorize the need to police social media into three broad buckets:
1) Obviously incorrect info, but especially those that can harm society. Fake moon landing videos fall here, but do not harm too much. However, stuff like antivax media, or ones that encourage people to not wear masks, or saying that Covid is fake. Or other more targeted things that tell people on voting days to go to the wrong place to vote, etc. Or obvious fakes where a politician says âoeI did not kill babiesâ and there is a edited video clip that just has the person saying the last phrase âoekill babies.â

2) Content which actively promotes concepts which we as a society have deemed unacceptable. Supporting of nazi images, swastika flags, or blatantly saying kill all Jews. Or blatant racisms, like content which says black people are less than human.

3) Content which actively incites violence. Says go kill this person. Doxes people. Says, oh it wouldd be a shame if something happened to this person.

If you think this is about a particular group and individual, notice, I have stayed clear of actively supporting any side in conventional political discourse, then think about why my remarks automatically conjure up an association for you.

Now, why is this policing of content necessary. Because, misinformation is worse than no information. It can be used and manipulated. At best, it results in a misinformed populace benignly making destructive decisions. At worst, it permits your worst enemies to use your own flaws to bring you down. The masses are susceptible to manipulation, emotional dog whistles, etc.

To quote MIB: A person is smart. People are dumb, panicky dangerous animals and you know it. Fifteen hundred years ago everybody knew the Earth was the center of the universe. Five hundred years ago, everybody knew the Earth was flat, and fifteen minutes ago, you knew that humans were alone on this planet. Imagine what you'll know tomorrow.

I felt so strongly about this, that by replying I'm undoing my moderation in this story.