On January 1^st, 2018, I made predictions about self driving cars, Artificial Intelligence, machine learning, and robotics, and about progress in the space industry. Those predictions had dates attached to them for 32 years up through January 1^st, 2050.

I made my predictions because at the time I saw an immense amount of hype about these three topics, and the general press and public drawing conclusions about all sorts of things they feared (e.g., truck driving jobs about to disappear, all manual labor of humans about to disappear) or desired (e.g., safe roads about to come into existence, a safe haven for humans on Mars about to start developing) being imminent. My predictions, with dates attached to them, were meant to slow down those expectations, and inject some reality into what I saw as irrational exuberance.

I was accused of being a pessimist, but I viewed what I was saying as being a realist. Today, I am starting to think that I too, reacted to all the hype, and was overly optimistic in some of my predictions. My current belief is that things will go, overall, even slower than I thought four years ago today. That is not to say that there has not been great progress in all three fields, but it has not been as overwhelmingly inevitable as the tech zeitgeist thought on January 1^st, 2018.

As part of self certifying the seriousness of my predictions I promised to review them, as made on January 1^st, 2018, every following January 1^st for 32 years, the span of the predictions, to see how accurate they were. This is my fourth annual review and self appraisal, following those of 2019, 2020, and 2021. I am an eighth of the way there!  Sometimes I throw in a new side prediction in these review notes.

The biggest news across these three fields this year is what appears to be a breakthrough into space tourism. For a few minutes at one point this year there were 19 people weightless in space at the same time and 8 of them were not professional astronauts. That happened on December 11^th. There were six people aboard Blue Origin’s New Shepherd in a sub-orbital flight, three Chinese astronauts on the Chinese Tiangong space station, seven regular crew members on the International Space Station (three from the US, two from Russia, and one each from Japan and the European Space agency), and three visitors to that same station, one a professional Russian astronaut, accompanying a Japanese billionaire and his publicity person. As we will see below there were two other orbital flights and three other sub-orbital flights with tourists on board in 2021.

UPDATE of 2019’s Explanation of Annotations

As I said in 2018, I am not going to edit my original post, linked above, at all, even though I see there are a few typos still lurking in it. Instead I have copied the three tables of predictions below from 2021’s update post, and have simply added a total of twelve comments to the fourth columns of the three tables. As with last year I have highlighted dates in column two where the time they refer to has arrived.

I tag each comment in the fourth column with a Cyan (#00ffff) colored date tag in the form yyyymmdd such as 20190603 for June 3^rd, 2019. In 2022 I have started highlighting the new text put in for the current year in LemonChiffon (#fffacd) so that it is easy to pick out this year’s updates.

The entries that I put in the second column of each table, titled “Date” in each case, back on January 1^st of 2018, have the following forms:

NIML meaning “Not In My Lifetime, i.e., not until beyond December 31^st, 2049, the last day of the first half of the 21^st century.

NET some date, meaning “No Earlier Than” that date.

BY some date, meaning “By” that date.

Sometimes I gave both a NET and a BY for a single prediction, establishing a window in which I believe it will happen.

For now I am coloring those statements when it can be determined already whether I was correct or not.

I have started using LawnGreen (#7cfc00) for those predictions which were entirely accurate. For instance a BY 2018 can be colored green if the predicted thing did happen in 2018, as can a NET 2019 if it did not happen in 2018 or earlier. There are five predictions now colored green, the same ones as last year, with no new ones in January 2020.

I will color dates Tomato (#ff6347) if I was too pessimistic about them. Last year I colored one date Tomato. If something happens that I said NIML, for instance, then it would go Tomato, or if in 2020 something already had happened that I said NET 2021, then that too would have gone Tomato.

If I was too optimistic about something, e.g., if I had said BY 2018, and it hadn’t yet happened, then I would color it DeepSkyBlue (#00bfff). None of these yet either. And eventually if there are NETs that went green, but years later have still not come to pass I may start coloring them LightSkyBlue (#87cefa). I did that below for one prediction in self driving cars this year.

In summary then: Green splashes mean I got things exactly right. Red means provably wrong and that I was too pessimistic. And blueness will mean that I was overly optimistic.

So now, here are the updated tables.

Self Driving Cars

Spoiler alert: very little movement in deployment of actual, for real, self driving cars.

Way back four years ago when I made my predictions about “self driving cars” that term meant that the cars drove themselves, and that there was no one in the loop at a company office, or following in a chase car, or sitting in the drive or passenger seat ready to take over or punch a big red button. As I documented in last year’s update the AV company’s conveniently neglect to mention these uncomfortable truths when they give press releases about their great progress. Even tech savvy people do not realize how very far all the breathless press stories about self driving cars being deployed are from being accurate about what is really going on. Last year I gave nine questions that every reporter should ask at any announcement by an AV car company. I have started seeing some similar questions being asked by more savvy reporters this last year. I have no idea whether any of them read my blog.

As an example of how companies gloss ofter things, if you carefully watch Waymo’s breathless “ooh” and “ah” filled video about their first deployment in San Francisco you will occasionally see the hands and knees of the safety driver sitting in the driver’s seat, conveniently unmentioned in the video that is trying to give the impression that their service is deployed. If you carefully read about the Chandler, Arizona, deployment and watch videos from there you will see how there is constant contact with people watching from home base, there are rescue vehicles that come and deposit a human driver to take over in some cases, and most importantly that the scale of the number of rides they give per week is tiny, way too few to be anything but a giant cash drain. That is two years after it was announced that the service was real and deployed. Nope.  Still very early days.

And (h/t Mohamed Amer) here is Cruise(GM)’s very first driverless pickup less that two months ago, in San Francisco.  Note that the passenger has two Cruise employees talking to him  through the car. The passenger is also a Cruise employee, and notes that he was not allowed by Cruise to bring his three year old along for the ride. And there is a camera person at both the pick up and drop off location. No indication of how far the vehicle drove driverless before or after this ride, nor whether there is a chase vehicle (with a safety person on board ready to stop the driverless vehicle) or not, nor whether it could operate at any other pick up or drop off location (Steve Jobs’ demos of new Apple technology were famously scripted down to every key press as otherwise he would hit some bug or the other).  This is an important step towards real deployment, but it is a long long way from real deployment.

The Hubris Of Self Driving Cars

In fact driverless cars, with safer roads, have been predicted again and again, for over 80 years, and it is always just around the corner. I recommend the book Autonorama by Peter Norton for some balance to the recent hype that really, really, autonomous cars are just around the corner once again.  It will make many techno people uncomfortable and even angry.  But it is good to have your internal beliefs challenged once in a while.

Norton points out that many companies have talked up the imminence of autonomous cars for a long time. Here is the list he documents for one such company, GM (which bought Cruise in 2016):

• 1939 World’s Fair at their Futurama exhibit, GM promised autonomous vehicles by 1960.
• 1964 World’s Fair in Futurama II, GM promised it again.
• 2010 GM and SAIC (Shanghai Automotive Industry Corporation) promised that it would arrive by 2030 as Xing! (autonomous Shanghai)
• 2017 GM 2017 Sustainability Report: Zero Crashes. Zero Emissions. Zero Congestion.

All worthy goals, but worthy goals doesn’t mean we yet no how to do it.  We certainly didn’t in 1939 and 1964. And we certainly didn’t know how to do it when Waymo started (inside Google) back in January 2009 (thirteen years ago and counting…).

We have seen the long tail of difficult situations delay deployments for years.  We know that humans can drive cars with “only” 35,000 annual fatalities just in the United States. We don’t know yet whether any of the sensor suites from any of the companies (all are different from the capabilities of human eyes) are sufficient to reach that goal or whether the presence of significant numbers of AVs on roads shared with humans will so impact the dynamics of driving that things get much worse.  We can have high faluting goals of zero this and zero that, but we actually have no idea whether the real world will allow that to happen without significant changes in approach.  WE JUST DON’T KNOW YET.

What we do know is that human drivers have many different capabilities, perceptual, reasoning, and an apparent ability to model what is happening and make predictions. Pure learning approaches are unlikely to capture that capability, so systems based largely on learning are making a big unproven bet. That hasn’t stopped massive funding going into two new startups in the last year, one for cars (Wayve) and one for trucks (Waabi) that try to solve the whole problem from pretty much a pure learning perspective. Didn’t we see this movie before? (Again, thanks to Mohamed Amer for pointing me at these two.)

Industry Misses

I believe that the hubris surrounding self driving cars has lead to delays in actual safety progress we could have already deployed if we had not been so distracted. I talk about this in the context of my own hubris after the table of the state of my predictions just below.

But first I will once again present a summary of predictions made by industry leaders that I scraped from a driverless future web site back in 2017. The site still exists but the last updates to it were made in late 2018. If you click on the predictions page you will see longer explanations of all these predictions that I gathered from it.

Each line is an individual prediction with the year it was made in parentheses. The orange arrows indicate that the companies later released updated later dates or cancelled their predictions completely, and that I personally noticed those updates. There may well be more that I have missed.

The years in blue indicate when the industry leaders thought these predictions would come to pass. I have highlighted all the dates up through 2021, now numbering 17 of the 23 predictions. Not one of them has happened or is even close to happening.

Hubris on a mass delusion scale. Audi fully autonomous by 2017?  That is Teslan in its delusion level.

Hubris

From my comments above the previous table it is clear that I think there has been too much hubris around driverless cars. But as I went through updating which predictions had stood the test of time I realized that I too had been guilty of hubris in my predictions.

There are two predictions about the earliest date that something might happen that I am now sure will not happen.  And it is not because of technical difficulty but because no one is working on them.

The first is about having dedicated lanes on freeways with special infrastructure installed that cars can interact with to make autonomous driving easier. Back in 1997 there was a project called the National Automated Highway System Consortium to do just that.  But when the hubris of fully self-driving cars made the need for external help for AVs seem redundant, such projects disappeared.

The second is the idea of leaving your car at the entrance to a garage and having it drive itself to park in a much tighter space than you could do.

Both these approaches to getting some sort of autonomy deployed in just a few years seem entirely doable to me, and I still believe that.  But the lure of full self driving (as in full self driving, not merely as a marketing ploy) drove everyone away from these approaches.

My hubris was to think that others would see things the same way and would work on them.  Given that no one was working on them do I really deserve credit for predicting they wouldn’t happen for a while?

Personal Gloating, An Irresistible Sin!!

Here I go.  For the last two years I have had a little rant that in April of 2019 the CEO of Tesla had said there would be one million autonomous Tesla taxis on the road by the end of 2020. Kai Fu Lee and I had agreed at the time to eat all such taxis on December 31^st, 2020. I.e., actually ingest them. All of them.  Of course there were zero such autonomous Tesla taxis on the road at the end of 2020. And a year later the number is still zero.

Note that in the last two weeks Tesla has agreed to eliminate the ability of drivers of their vehicles to play video games on the car’s display screen while the car is in motion. This was under regulatory pressure, indicating that “Full Self Driving” software is still not fully self driving. The latest spin from Tesla is that of course the words in the name of the software, “Full Self Driving”, could not possibly be interpreted to mean full self driving.

Defending Against Slings And Arrows, Another Irresistible Sin!!

Earlier this year someone used the fact that my prediction for when electric vehicles would account for 30% of automobile sales in the US as being no early than the year 2027 (see table above) as proof that I am a pessimist. Therefore, they said, my predictions about autonomous vehicles could not be trusted.

EV sales in the US were 1.7% of the total market in 2020 (up from 1.4% in 2019). We’ll need four doublings of that in seven years to get to 30%. It may happen. It may happen sooner than 2027.  But not by much. It would be a tremendous sustained growth rate that we have not yet seen.

Robotics, AI, and Machine Learning

With respect to my predictions for AI and ML there are only three that come close to being in play this year, either in terms of work that was done that impacts my predictions, or the date is close to when I said that something would or would not happen. I have annotated the table below in those three places; the next big thing, change in perspective of how to measure AI success, and robots that can really get around in our houses in a general purpose way.

The Next Big Thing

Back in 2018 I predicted that “the next big thing”, to replace Deep Learning, as the go to hot topic in AI would arrive somewhere between 2023 and 2027.  I was convinced of this as there has always been a next big thing in AI.  Neural networks have been the next big thing three times already. But others have had their shot at that title too, including (in no particular order) Bayesian inference, reinforcement learning, the primal sketch, shape from shading, frames, constraint programming, heuristic search, etc.

We are starting to get close to my window for the next big thing.  Are there any candidates?  I must admit that so far they all seem to be derivatives of deep learning in one way or another.  If that is all we get I will be terribly disappointed, and probably have to give myself a bad grade on this prediction.

So far the things that I see bubbling around and getting people excited are transformers, foundation models, and unsupervised learning.

Transformers provide a front end to deep learning that lets it handle sequential data rather than all input at once. This lets it be applied to natural language and to image sequences. We have seen tremendous hype about large language models using this mechanism. There is something going on there, but nothing as astounding or important as people imagine.

These language models are over interpreted by people as understanding what they are spitting out, especially when the press writes stories where they have cherry picked responses. But they come with incredible problems, including copyright violations, intellectual theft of code, and even outright life threatening danger when they find their way into consumer products. Tech companies have a real problem in rushing some of these systems to market.

Overall the will to believe in the innate superiority of a computer model is astounding to me, just as it was to others back in 1966 when Joseph Weizenbaum showed off his Eliza program which occupied a just a few kilobytes of computer memory. Joe, too, was astounded at the reaction and spent the rest of his career sounding the alarm bells. We need more push back this time around.

Foundation models are large trained models that start out as a basis for tuning particular applications.  There has been some self important announcements with a sort of me too feel (“Hey, I produced a foundation model too!!”), which don’t amount to much of an intellectual contribution.  If this turns out to be the next big thing I am going to have to rip off my mask of equanimity and revert to my natural state of being a grumpy old man.

Unsupervised learning is an idea that has been around for a long time.  Not a big intellectual jump to want to get it into deep learning–may be a hard technical problem, but not an intellectual breakthrough this time around.  (RIP Teuvo Kohonen who passed away last month.)

So, am I wrong, or was I just too optimistic about how soon the next big thing will show up?  I’ve given myself another 28 years of reviewing my predictions so perhaps there is time for something real and big to show up, and then I’ll be able to claim that I was simply too optimistic…

Beyond The Turing Test And Asimov’s Laws 

I start to see the serious technical press really trying to come to terms with judging whether systems are intelligent or not, and the number of articles questioning the Turing Test seem to be on the rise. Perhaps this is driven by the perceptions of what transformer based natural language systems can do. They are not intelligent but they can can fool an awful lot of the people an awful lot of the time.

As is often the case, Melanie Mitchell has a really good analysis, this time on the Turing Test and GPT-3. She is a renowned academic but she now also writes for Quanta Magazine.

Getting Around In A House

The first mass market robots that could get around reliably in ordinary people’s houses were introduced by my company iRobot at a two hundred dollar price point twenty years ago this year, in September of 2002. iRobot has now sold around 35 million Roombas and I am guessing that the follow on brands have together sold about the same number.

But we all know that Roombas could never be Rosie the Robot from the cartoon series The Jestsons. They are limited to a single flat floor and can’t get up high enough to really see objects that matter to humans in the houses that they bumble around in.  One of my predictions is that A robot that can navigate around just about any US home, with its steps, its clutter, its narrow pathways between furniture, etc. won’t even be a lab demo until 2026, and not be deployed for real until 2030 and at low cost in 2035.

Amazon just made a good and necessary step towards this capability with release of the Astro home robot. I got to spend time with some Astros before they were announced and I was impressed with the technical capabilities and at their price for all that technology. Whether they will be a successful product or not I can not hazard a guess.

The impressive step that Amazon has made is in getting a really reliable SLAM (Simultaneous Localization And Mapping) system that can quickly build a very good map of a house without any help from humans. It starts up in an unknown place in an unknown environment, and within minutes has a solid useable map. It is still somewhat low to the ground but its camera on a mast lets it get views up to table top level. No way to handle steps yet, and it mostly avoids clutter, but this is definitely progress.

The Problem As I See It

I have often stated that I think the field of AI, despite the great practical successes recently of Deep Learning, is probably a few hundred years away from where most people think it is. We’re still back in phlogiston land, not having yet figured out the elements, including oxygen.

AI, Robotics, and Machine Learning are areas that I have a real personal investment in.  I wrote a terrible Masters thesis on ML back in 1977.  I joined the Stanford AI Lab later that year, then the MIT AI Lab four years later, and became director of that lab in 1997, merging it with LCS (Lab for Computer Science) to form MIT CSAIL in 2003, the largest lab at MIT, still today.  I have founded six AI and robotics companies.  After 45 years in the academic and industry trenches can I be unbiased?  Probably not.

I know that many who disagree with me will dismiss me for all that experience that I have. Perhaps those who agree with me should also dismiss me for the same reason!!

My current belief is that it all gets back to the symbol grounding problem, and even more deeply to adopting a computational approach to AI, Robotics, and ML (and I expect almost no one will agree with that latter claim).

Traditional AI put off the very hard problem of perception, and assumed that later that would be solved and a perception system would deliver symbols describing the world. What are symbols? Well, a convenient abstraction was Lisp symbols, and Lisp symbols really come down to whether two things have the same address in computer memory (with some technical fixes for copying garbage collectors moving stuff around everywhere…), with a little bit of dictionary like descriptions of each symbol in terms of other symbols. (Real dictionaries used by real people always bottom out in the physical experience of those people; not so a Lisp program.) Perhaps that certainty of symbols and separation from perception, and even the metaphor of computation, has lead people astray in the way they try to solve the problems of common sense, qualitative reasoning, inference, etc.  Many dear friends may take this as an attack on their work, but that is not how I mean it.  I mean it as more general questioning.

Along came Deep Learning and many thought that the perception problem had been solved, and that DL gave us those symbols, recognizing objects in the world. But I don’t think that is right either. I think what DL has done is solve the labeling problem, not the object recognition problem.  It gives good label, and it gives poor object. (And yes I meant label and object rather than labels and objects.)

If you want to expand your mind, try reading The Promise Of Artificial Intelligence by Brian Cantwell Smith. This is by far his shortest and most readable book from his forty years of producing them. It is exceptionally erudite. But it is still a hard slog, and even native English speakers will be consulting a dictionary quite often.

In this book Smith introduces the idea of registration, as a maintained relationship between an object outside of us and what goes on inside our head (and he would have it also in a classical computer) despite changes in perception and even context.  It feels a bit like autopoiesis for thinking rather than Maturana and Varela’s conception of it for being. It is a hard concept to grasp and hold onto.  It will be even harder to make it real.  But perhaps that is what AI will need.

And yes this little section is obscure, but the best I can do for a rather short blog post.

Space

There are three big stories in the space industry from 2021, and they all impact one or more predictions that I made.  The first is that space tourism notched up significantly from where it had been, both for sub-orbital and orbital trips.  The second is that Boeing had another serious setback on launching people into space; I had said by 2022, now I think that is less likely to happen.  And third, a lot of visibility into progress on SpaceX’s Starship (what was once called the BF Rocket) second stage progress in the first half of the year, and much less than people had expected for the first stage, with no launch yet.

Space Tourism Sub-Orbital

There were two players in each of sub-orbital and orbital space tourism.  Virgin Galactic and Blue Origin for sub-orbital, and Roscosmos and SpaceX for orbital trips.

Virgin Galactic had two manned launches of their sub-orbital Unity each of which got to a bit less than 90km in altitude (note that way back in 2004 Virgin Galactic had three manned flights that got over 100km). The first of this year’s flights was with two professional pilots only, but then on July 11^th two pilots were joined by four civilians, including billionaire founder Richard Branson. This flight was only announced after Jeff Bezos had stated his intention to get into space as the first sub-orbital tourist, and Branson rushed in and beat him by nine days.  All six people on board were part of the Virgin Galactic organization, so none paid for their flights explicitly.

Blue Origin was a little like an actual tourism company in that all three of its manned sub-orbital space flights in 2021 had between one and four paying customers on board, though each launch also had non-paying celebratory guests. Jeff Bezos was on board the first of these flights which launched on July 20^th, the 52^nd anniversary of Armstrong and Aldrin landing on the Moon.  A total of 14 people flew on these three flights, none of whom were professional space travelers. As best as I can tell six of those seats were paid for. Each of the Blue Origin flights topped 100km in altitude.

This is the first year that there have been paying customers on sub-orbital flights and it looks like six of them had paid seats, so not yet the few handfuls of paying customers that I predicted would not happen until at least 2022. So my prediction of no earlier than 2022 was correct. Blue Origin looks like it has a chance to get a few handfuls of paying customers in 2022, and we’ll see whether Virgin Galactic launches its first paying customer this coming year.

There is a question in my mind whether there will be enough appetite for these very short experiences of only about five minutes of weightlessness.  The price per minute is much more than the rate for an orbital flight. Perhaps that will push the market more towards orbital tourism. In any case both the companies involved have much bigger space aspirations than just sub-orbital flights and may lose interest if either the demand or profit margins are not high enough. Sub-orbital tourism may never reach a breakneck pace as airplane pleasure flights did long ago–the market will decide.

Space Tourism Orbital

In 2021 we saw eight orbital space tourists, two of whom paid for their own rides, however all eight rides were paid for, unlike the sub-orbital cases.  The only previous space tourists were eight orbital seats, spread over seven individuals, between 2001 and 2009. In this one year, 2021, we had a doubling of the number of all time orbital space tourists.

The first orbital tourist flight of 2021 was carried out in September by SpaceX, when four people launched on the third flight of a Falcon 9 booster, aboard the second flight of a Dragon capsule. They were aloft just under 72 hours, or three days. One of the people onboard paid for the flight, and the other three were his guests. This is the only fully commercial orbital flight ever to date.

All previous orbital tourist flights had been on Soyuz vehicles going to the International Space Station (ISS), operated by Roscosmos, the same organization that sends all other Russian manned flights aloft.  The other four 2021’s tourists were on those same vehicles to the same destination, though three of the people were working for their flights. In October a Russian actor along with a movie director/camera operator to film her journeyed to shoot scenes for a movie. In December a Japanese billionaire, Yusaku Maezawa, along with his publicist, also went to the ISS. Both visits lasted 12 days. Maezawa is also signed up to loop around the Moon with SpaceX in 2023, delayed from the original announced goal of the fourth quarter of 2018.

Clearly 2021 was the best year ever for space tourism.

Boeing’s Woes

NASA funded SpaceX and Boeing to develop two reusable capsules for launching NASA astronauts. Originally they were neck and neck on schedule. Each had some setbacks and things have taken longer than expected.

The SpaceX vehicle, Crew Dragon, first took people to space in 2020, and has now done so five times, including hosting the first non-Russian orbital tourist flight.

Boeing has not been so lucky. In their first unmanned orbital test flight in December 2019 there were serious software problems and the vehicle did not reach the ISS as planned.  During an August 2021 launch window for a re-fly of the unmanned test, problems with valves deep in the craft were discovered and it was removed from the launch pad. Current plans call for a launch in May 2022, but that is by no means certain.  Meanwhile NASA has reassigned crew members from each of the first two manned launches as those astronauts were getting stale waiting for Boeing to fix the problems. This is not a good sign.

Starship

SpaceX Starship (as distinct from Boeing Starliner) was an a very rapid launch, blow-up, try again, pace as 2020 turned into 2021. Starship will be the largest rocket ever flown and both its first and second stage are intended to be fully re-usable.

After a series of fiery flights which resulted in loss of vehicle, usually in spectacular explosions, SpaceX launched the second stage of Starship in early May 2021 and had it land softly back at the launch pad, remain standing, and survive without blowing up.

Attention shifted to the first stage and people were expecting it to launch soon, but it was not to be in 2021.  Three times, a second stage has been mated to a first stage, and sometimes a first stage has fired some engines in a static test. So far, however, there has been no launch attempt.  It thus remains to be seen whether the lessons from the second stage can lead to a faster test campaign for the first stage.

As usual with the CEO of SpaceX it is sometimes hard to discern fact from fantasy/trolling.  On November 30th (in the middle of the Thanksgiving break) he warned of potential bankruptcy for SpaceX unless all hands were on deck to fix problems with producing enough Raptor engines for Starship’s first stage. In the linked story “according to Musk’s email, SpaceX needs to launch Starship at least once every two weeks next year to keep the company afloat”. Next year in that context is 2022.

As a comparison, SpaceX launched its first Falcon 9 in June 2010.  They did not reach an annual rate of one launch every two weeks until 2020 with 26 launches. That was a ten year ramp up. So to go from zero launches to a run rate of one every two weeks by the end of the year seems rather ambitious even by SpaceX record breaking standards. [[The Falcon 9 launch rate in 2021 improved to once every 12 days.]]

An Astounding Historical Rocket Ramp Up

Incidentally, though by modern standards SpaceX develops and deploys at incredible speed, there is one historical instance of even faster rocket development, production, and deployment.

Werner Von Braun designed the V-2 rocket (originally designated A-4), and carried out the first test flight on October 3^rd, 1942. That sub-orbital flight reached an altitude of 192km, roughly twice what Virgin Galactic and Blue Origin’s suborbital rockets have achieved.

The V-2 is the progenitor of all human spaceflight rockets, and the team that designed and produced them ended up as principals in both the Soviet and US space programs. Werner von Braun was the architect of the Apollo lunar landing program. However, back in 1942 he promoted the V-2 as a weapon, and by December of 1942 Hitler ordered them into mass production.

Over the next two years there were more than 300 further test flights, and then in the last few months of the war, starting on September 8^th 1944 in an attack on newly liberated Paris, Germany launched over 3,000 of them on operational warhead delivery missions.  Three thousand operational flights in eight months.