10 ms·

Learning to think critically about machine learning

153 points by SleekEagle 4y ago

lacker 4y ago
It's interesting to look at the actual course content here. https://ocw.mit.edu/courses/res-tll-008-social-and-ethical-responsibilities-of-computing-serc-fall-2021/resources/res-tll008f21-6036_lab2/ https://ocw.mit.edu/courses/res-tll-008-social-and-ethical-r... More generally, what does it mean for a model to be "fair"? LIT Company’s Definition of Fairness (Group Unaware): The company believes that a fair process and, therefore, a fair model, would not account for gender or race at all. Advocacy Group's Definition (Demographic parity): An advocacy group believes that a model is fair if the distribution of outcomes for each demographic, gender, or other subgroup is the same among those that applied and those that were accepted. For example, in the example above, 30% of the applicants for loan applications come from women. In the demographic parity definition of fairness, this means 30% of the approved loan applications should come from women. I feel like the course content is somewhat slanted here. It is missing the definition of "fairness" in which you treat race and gender just like any other feature. Many systems work this way in practice - for example car insurance charges you differently by gender, because the statistics for genders are different. Ad-matching by gender and race is a longstanding practice. And any new system that you just train from scratch, by default it will not know to treat gender or race different from anything else. It is an interesting question, though. The main problems, I think, are practical ones - large enough AI models cannot be "race-blind" because if you remove race as a feature, they will be able to infer it anyways from proxy features. Whereas the only real way to enforce a system achieves the same percentage results for different groups is to add a "quota system" where you explicitly use different thresholds for different groups. So the practical alternatives often become "quota" or "nothing".
- andersource 4y ago
  Overall agree, although regarding > large enough AI models cannot be "race-blind" because if you remove race as a feature, they will be able to infer it anyways from proxy features In theory using a gradient reversal layer and an adversarial classifier you could do just that, to an extent. It could hurt the model's performance, which is exactly your point (should we ignore features with signal because they can be used to discriminate.)
- wolverine876 4y ago
  > It is missing the definition of "fairness" in which you treat race and gender just like any other feature. The real issue here, of course, is whether they are just like every other feature: Certainly in our society they are not perceived that way. People perceive very serious issues and have very strong feelings around race and gender. We see that right here on HN, of course. There is also, of course, a lot of discrimination by humans based on race and gender. If we want an unbiased, fair (and accurate) system, we have to correct for that. And the discrimination creates higher order effects: If there is discrimination against group X in K-12 education funding, then fewer of X will go to college, and fewer will have higher-paying jobs. If we then select blindly for income, we incorporate that bias (which might be appropriate if studying income by group, but not if we use it as a proxy for intelligence or effort). > the practical alternatives often become "quota" or "nothing". Those aren't practical alternatives, they are logical extremes creating a Manichean choice. Those are alternatives or a political debate, not for practical problem-solving.
- RangerScience 4y ago
  I'm pretty fascinated by all of this, although have barely dipped my toes in. IMHO - "Fairness" is a technical term-of-art meaning "the outcome _should not_ be effected by inputs X, Y and Z", and the collected science around making a system behave that way. It closely but not quite matches the colloquial meaning, ie "that's not fair!" - kinda like how "a fair coin" has a specific meaning that mostly tracks with how people use the word, but not quite, and with a lot more specificity. It's typically applied when you want to correct for real-world "unfair biases" in the training data; which in practical application is typically race, gender and the other legally protected categories - but AFAIK is just whichever inputs you decide you want to not have an impact on the outcome. AFAIK what you get out of the AI/ML "fairness" science is a way to measure, and correct for, dependency on the inputs that you (exterior to the system) have decided that you want to _not_ impact the outcome.
- pfortuny 4y ago
  The second definition assumes that the law of large numbers applies to any instance. It is impossible to satisfy each and every time. And it may also be blind to inherenet inequalities (as insurance companies know).
- walnutclosefarm 4y ago
  True story: at the height of fear about overrunning ICU facilities for Covid-19, a hospital for which a close relative works built a statistical model to predict which patients would benefit most from the ICU. The idea was that if there weren't sufficient beds, they'd triage so the beds went to those most likely to be saved from death, through the ICU interventions. The hospital's diversity committee wanted to modify the model so that a population-matching fraction of various minority groups were always selected (your quota). When told that the model didn't consider race, and that in order to achieve their desired goal, race would have to be added to the model, and artificial weights attached to race in order to bring things into balance (which would be a violation of Federal Law, BTW, as it directly and in a fully documented fashion discriminated in providing care based on race), the response was "That can't be true. If the model worked fairly races would be be treated equally." But it absolutely was true - because some minorities are far more likely to have, and to have untreated comorbidities that worsen outcomes for Covid-19, the model selected fewer of those minorities for the ICU. So, yeah, for the Diversity Committee definition of fairness, you need quotas.
- gmac 4y ago
  > for example car insurance charges you differently by gender, because the statistics for genders are different Interestingly, car insurers in the EU are no longer allowed to differentiate premiums by gender. But since this law came in, the difference between average male and female premiums has widened, based on correlated risk factors, such as occupation and model of car. https://www.theguardian.com/money/blog/2017/jan/14/eu-gender-ruling-car-insurance-inequality-worse https://www.theguardian.com/money/blog/2017/jan/14/eu-gender...
faangiq 4y ago
Critical thinking is for poor people.
ggm 4y ago
It's easy to get down voted introducing either skepticism or social critique into other discussions here. I know, because I do. Sometimes I own it, (as in, I think it was justified complaint I'm not helping the conversation) but other times, its like interfering with the magical blue sky dreaming incurs a penalty, but I think grounded is better. Most ML is stats and phase-change to find parameters to work on. Optimisation is not a black art, its Operations-Research by another name. I blame the rise of Kurzeweil. Magical realism was a great literary trend, but applying it to machine learning and consciousness and the uplift is not helpful.
xyzzy21 4y ago
It would also be nice to remove the "magically thinking" around machine learning. It's mathematically related to all prior signal processing techniques (mostly a proper superset) but it also have fundamental limits that no one talks about seriously. ML et al. are NOT MAGIC but they are treated as if they were. And that is in itself a dangerous moral and ethical lapse.
- wolverine876 4y ago
  People will (and I'm sure do) use this magical thinking politically, persuading people to trust the computer and therefore, unwittingly, trust the persons who control the computer. That, to me, is the greatest threat - it is an obvious way to grab power, and most people I know don't even question it. It's a major consequence of mass public surveillance.
  - heavyset_go 4y ago
    Bureaucracies would love for a blackbox to delegate all of their decisions and responsibilities to in an effort to shift liability away from themselves. You can't be liable for anything, you were just doing what the computer told you to do, and computers aren't fallible like people are.
- bell-cot 4y ago
  In my wishful thinking, by far the best way to do that would be for the courts to stick companies with full legal liability for the shortcomings of their "machine learning" systems. And if it's fairly easily demonstrate that GiantCo's ML decision making system is a sexist, racist, ageist...then GiantCo is not just guilty, but also presumed to have known in advance that they were systematically and deliberately on the wrong side of the law.
- arcticfox 4y ago
  > ML et al. are NOT MAGIC but they are treated as if they were. They're not magic - nothing is, but what are they? > but it also have fundamental limits that no one talks about seriously What are these fundamental limits? 20 years ago I imagine skeptics in your camp would have set these "fundamental" limits at lower than DALL-E 2, GPT-3, AlphaStar etc. Or are you talking about limits today? In which case, sure, but I think "fundamental" is the wrong word to use there given they change continuously. > It's mathematically related to all prior signal processing techniques (mostly a proper superset) And human brains are what if not signal processing machines?
photochemsyn 4y ago
ML is really cool technology with incredible applications and potential. For example, biological genomes can now be sequenced relatively easily but finding the actual protein-coding sequences hidden in these massive genome sequences can be difficult. ML provides some novel approaches to this problem, and can potentially markup these genomes and even classify the probable structure/function of the resulting proteins. Amazing stuff, really. However, anyone who thinks this tech couldn't go off the rails in the hands of nefarious actors should go read Ed Black's "IBM and the Holocaust" or Josef Teboho Ansorge's "Identify and Sort".
SemanticStrengh 4y ago
I would prefer machine learning to learn critical thinking
- astrange 4y ago
  GANs have that ;) ML doesn't have deduction though, it just has induction (and that's usually the training to make the model, not the model itself).
oxff 4y ago
It is just a super massive graph optimization, don't get confused about it like the OpenAI guys or whoever thinks matrix multiplication is achieving consciousness .
- SleekEagle 4y ago
  Why are the two necessarily unrelated? Can human being just be considered to be learning via optimization, and perhaps consciousness is an emergent property of an agent with a large enough world model, or a world model that includes the agent itself? While I don't think a majority really thinks current systems are conscious, SOTA results are absolutely astounding (check out DALL-E 2 if you haven't seen it already). Whether or not an agent is conscious doesn't really matter from a practical standpoint (but obviously a moral one) in the long run - it is intelligence that matters with these agents, and they're getting absurdly more intelligent by the half-decade
  - oneoff786 4y ago
    Feelings aren’t an emergent property of intelligence, with intelligence being defend as some nth derivative of optimization capability. So I think no. Human consciousness isn’t just a brain. It’s a system of which the brain is a part, occurring through time.
  - astrange 4y ago
    A human being has an online learning system (which ML models usually don't) but it isn't just that. Even just the brain has emotions, motivation, hunger, self-interest, forgetting, some instinctual desires, and not only do those support the way humans are intelligent/conscious, but it's possible they're necessary for it, and you can't give them to an "AGI" without it losing the computer-like usefulness of an ML model.
  - SemanticStrengh 4y ago
    We are on an AGI winter in NLU. Cool openAI demos are cool and irrelevant. The HN crowd should really learn to go see the leaderboards by himself on paperswithcode.com then he would realize the reality, we are stagnant on the key basic tasks (e.g coreference resolution). While GPT-3 is just a subtile bullshit generator that push to its paro(t)xism the illusion of understanding that mere collocated statistics amalgamation provide, Dalle-2 on the other hand is very impressive but it does not advance the key NLU tasks and just show how far a smart trick (constrastive learning) can go before plateau-ing. The idea consciousness emerge proportionately with the accuracy of your mental isomorphic world representation is cute however we don't become more conscious by becoming more erudite, and the most intense magical qualias, such as e.g orgasms are accessible to the simplest mammals and are unrelated to activities in the higher cognitive regions of the brain. Even a newborn that has no understanding of its surrounding experience qualias.
jimbokun 4y ago
I'm not sure engineering types will be satisfied with these kinds of conclusions: > “It is not someone else’s job to figure out the why or what happens when things go wrong. It is all of our responsibility and we can all be equipped to do it. Let’s get used to that. Let’s build up that muscle of being able to pause and ask those tough questions, even if we can’t identify a single answer at the end of a problem set,” Kaiser says. Hopefully the actual course content is more concrete than this. But this kind of language strikes me as encouraging people to feel the "correct" way about a problem, but not really emphasizing coming up with concrete, actionable solutions. And without actionable solutions, I feel like the value of this content would be very limited.
noasaservice 4y ago
Perhaps this is a hot take, but when I see ML being used (for the most part), it's because people do not fundamentally understand OR know how to solve the problem in question. And instead of understanding what's going on, and solving the problem efficiently, instead it's "throw GB's or TB's of data at an algo that we tweak and hope for the best". Sure, it gets results for massive processing times and data. And sure it's "fuzzy" and can fail on really stupid stuff and provide bad answers confidently. But it'll get the next VC funding line, won't it?
- axg11 4y ago
  Let's look at a few of the most impressive applications of machine learning: - Classification in computer vision - Protein folding (AlphaFold) - Image generation (Dall-E 2) - Answering general language queries (GPT-3) It's unclear how any of these applications could have been tackled _without_ machine learning. We had no solid grasp on these problems before ML, with the exception of protein folding. I think you are being cynical. Of course not every ML project will result in a home run success. Should that make us sceptical of the entire field?
  - Jensson 4y ago
    But most people using neural nets in the industry aren't working on those things. They just slap it on any data inference task, even though in most cases traditional statistical/data science models works much better.
    - r-zip 4y ago
      In my experience, people use appropriate tools for the job. Most data scientists won't reach for a neural network if the problem can be solved by other, simpler means.
      - Jensson 4y ago
        Which is why I said "using neural nets" instead of "data scientist".
jasfi 4y ago
For those interested in NLU/AGI, see LxAGI: https://lxagi.com https://lxagi.com. No demo available just yet.
- ShamelessC 4y ago
  Looks like vaporware.
  - jasfi 4y ago
    The MVP is being developed, just not yet ready for release. The landing page is for those who want to sign-up to be notified of news.

153 points by SleekEagle 4y ago

It's interesting to look at the actual course content here. https://ocw.mit.edu/courses/res-tll-008-social-and-ethical-responsibilities-of-computing-serc-fall-2021/resources/res-tll008f21-6036_lab2/ https://ocw.mit.edu/courses/res-tll-008-social-and-ethical-r... More generally, what does it mean for a model to be "fair"? LIT Company’s Definition of Fairness (Group Unaware): The company believes that a fair process and, therefore, a fair model, would not account for gender or race at all. Advocacy Group's Definition (Demographic parity): An advocacy group believes that a model is fair if the distribution of outcomes for each demographic, gender, or other subgroup is the same among those that applied and those that were accepted. For example, in the example above, 30% of the applicants for loan applications come from women. In the demographic parity definition of fairness, this means 30% of the approved loan applications should come from women. I feel like the course content is somewhat slanted here. It is missing the definition of "fairness" in which you treat race and gender just like any other feature. Many systems work this way in practice - for example car insurance charges you differently by gender, because the statistics for genders are different. Ad-matching by gender and race is a longstanding practice. And any new system that you just train from scratch, by default it will not know to treat gender or race different from anything else. It is an interesting question, though. The main problems, I think, are practical ones - large enough AI models cannot be "race-blind" because if you remove race as a feature, they will be able to infer it anyways from proxy features. Whereas the only real way to enforce a system achieves the same percentage results for different groups is to add a "quota system" where you explicitly use different thresholds for different groups. So the practical alternatives often become "quota" or "nothing".

andersource 4y ago
Overall agree, although regarding > large enough AI models cannot be "race-blind" because if you remove race as a feature, they will be able to infer it anyways from proxy features In theory using a gradient reversal layer and an adversarial classifier you could do just that, to an extent. It could hurt the model's performance, which is exactly your point (should we ignore features with signal because they can be used to discriminate.)
wolverine876 4y ago
> It is missing the definition of "fairness" in which you treat race and gender just like any other feature. The real issue here, of course, is whether they are just like every other feature: Certainly in our society they are not perceived that way. People perceive very serious issues and have very strong feelings around race and gender. We see that right here on HN, of course. There is also, of course, a lot of discrimination by humans based on race and gender. If we want an unbiased, fair (and accurate) system, we have to correct for that. And the discrimination creates higher order effects: If there is discrimination against group X in K-12 education funding, then fewer of X will go to college, and fewer will have higher-paying jobs. If we then select blindly for income, we incorporate that bias (which might be appropriate if studying income by group, but not if we use it as a proxy for intelligence or effort). > the practical alternatives often become "quota" or "nothing". Those aren't practical alternatives, they are logical extremes creating a Manichean choice. Those are alternatives or a political debate, not for practical problem-solving.
RangerScience 4y ago
I'm pretty fascinated by all of this, although have barely dipped my toes in. IMHO - "Fairness" is a technical term-of-art meaning "the outcome _should not_ be effected by inputs X, Y and Z", and the collected science around making a system behave that way. It closely but not quite matches the colloquial meaning, ie "that's not fair!" - kinda like how "a fair coin" has a specific meaning that mostly tracks with how people use the word, but not quite, and with a lot more specificity. It's typically applied when you want to correct for real-world "unfair biases" in the training data; which in practical application is typically race, gender and the other legally protected categories - but AFAIK is just whichever inputs you decide you want to not have an impact on the outcome. AFAIK what you get out of the AI/ML "fairness" science is a way to measure, and correct for, dependency on the inputs that you (exterior to the system) have decided that you want to _not_ impact the outcome.
pfortuny 4y ago
The second definition assumes that the law of large numbers applies to any instance. It is impossible to satisfy each and every time. And it may also be blind to inherenet inequalities (as insurance companies know).
walnutclosefarm 4y ago
True story: at the height of fear about overrunning ICU facilities for Covid-19, a hospital for which a close relative works built a statistical model to predict which patients would benefit most from the ICU. The idea was that if there weren't sufficient beds, they'd triage so the beds went to those most likely to be saved from death, through the ICU interventions. The hospital's diversity committee wanted to modify the model so that a population-matching fraction of various minority groups were always selected (your quota). When told that the model didn't consider race, and that in order to achieve their desired goal, race would have to be added to the model, and artificial weights attached to race in order to bring things into balance (which would be a violation of Federal Law, BTW, as it directly and in a fully documented fashion discriminated in providing care based on race), the response was "That can't be true. If the model worked fairly races would be be treated equally." But it absolutely was true - because some minorities are far more likely to have, and to have untreated comorbidities that worsen outcomes for Covid-19, the model selected fewer of those minorities for the ICU. So, yeah, for the Diversity Committee definition of fairness, you need quotas.
gmac 4y ago
> for example car insurance charges you differently by gender, because the statistics for genders are different Interestingly, car insurers in the EU are no longer allowed to differentiate premiums by gender. But since this law came in, the difference between average male and female premiums has widened, based on correlated risk factors, such as occupation and model of car. https://www.theguardian.com/money/blog/2017/jan/14/eu-gender-ruling-car-insurance-inequality-worse https://www.theguardian.com/money/blog/2017/jan/14/eu-gender...

faangiq 4y ago

Critical thinking is for poor people.

ggm 4y ago

It's easy to get down voted introducing either skepticism or social critique into other discussions here. I know, because I do. Sometimes I own it, (as in, I think it was justified complaint I'm not helping the conversation) but other times, its like interfering with the magical blue sky dreaming incurs a penalty, but I think grounded is better. Most ML is stats and phase-change to find parameters to work on. Optimisation is not a black art, its Operations-Research by another name. I blame the rise of Kurzeweil. Magical realism was a great literary trend, but applying it to machine learning and consciousness and the uplift is not helpful.

xyzzy21 4y ago

It would also be nice to remove the "magically thinking" around machine learning. It's mathematically related to all prior signal processing techniques (mostly a proper superset) but it also have fundamental limits that no one talks about seriously. ML et al. are NOT MAGIC but they are treated as if they were. And that is in itself a dangerous moral and ethical lapse.

wolverine876 4y ago
People will (and I'm sure do) use this magical thinking politically, persuading people to trust the computer and therefore, unwittingly, trust the persons who control the computer. That, to me, is the greatest threat - it is an obvious way to grab power, and most people I know don't even question it. It's a major consequence of mass public surveillance.
- heavyset_go 4y ago
  Bureaucracies would love for a blackbox to delegate all of their decisions and responsibilities to in an effort to shift liability away from themselves. You can't be liable for anything, you were just doing what the computer told you to do, and computers aren't fallible like people are.
bell-cot 4y ago
In my wishful thinking, by far the best way to do that would be for the courts to stick companies with full legal liability for the shortcomings of their "machine learning" systems. And if it's fairly easily demonstrate that GiantCo's ML decision making system is a sexist, racist, ageist...then GiantCo is not just guilty, but also presumed to have known in advance that they were systematically and deliberately on the wrong side of the law.
arcticfox 4y ago
> ML et al. are NOT MAGIC but they are treated as if they were. They're not magic - nothing is, but what are they? > but it also have fundamental limits that no one talks about seriously What are these fundamental limits? 20 years ago I imagine skeptics in your camp would have set these "fundamental" limits at lower than DALL-E 2, GPT-3, AlphaStar etc. Or are you talking about limits today? In which case, sure, but I think "fundamental" is the wrong word to use there given they change continuously. > It's mathematically related to all prior signal processing techniques (mostly a proper superset) And human brains are what if not signal processing machines?

photochemsyn 4y ago

ML is really cool technology with incredible applications and potential. For example, biological genomes can now be sequenced relatively easily but finding the actual protein-coding sequences hidden in these massive genome sequences can be difficult. ML provides some novel approaches to this problem, and can potentially markup these genomes and even classify the probable structure/function of the resulting proteins. Amazing stuff, really. However, anyone who thinks this tech couldn't go off the rails in the hands of nefarious actors should go read Ed Black's "IBM and the Holocaust" or Josef Teboho Ansorge's "Identify and Sort".

SemanticStrengh 4y ago

I would prefer machine learning to learn critical thinking

astrange 4y ago
GANs have that ;) ML doesn't have deduction though, it just has induction (and that's usually the training to make the model, not the model itself).

oxff 4y ago

It is just a super massive graph optimization, don't get confused about it like the OpenAI guys or whoever thinks matrix multiplication is achieving consciousness .

SleekEagle 4y ago
Why are the two necessarily unrelated? Can human being just be considered to be learning via optimization, and perhaps consciousness is an emergent property of an agent with a large enough world model, or a world model that includes the agent itself? While I don't think a majority really thinks current systems are conscious, SOTA results are absolutely astounding (check out DALL-E 2 if you haven't seen it already). Whether or not an agent is conscious doesn't really matter from a practical standpoint (but obviously a moral one) in the long run - it is intelligence that matters with these agents, and they're getting absurdly more intelligent by the half-decade
- oneoff786 4y ago
  Feelings aren’t an emergent property of intelligence, with intelligence being defend as some nth derivative of optimization capability. So I think no. Human consciousness isn’t just a brain. It’s a system of which the brain is a part, occurring through time.
- astrange 4y ago
  A human being has an online learning system (which ML models usually don't) but it isn't just that. Even just the brain has emotions, motivation, hunger, self-interest, forgetting, some instinctual desires, and not only do those support the way humans are intelligent/conscious, but it's possible they're necessary for it, and you can't give them to an "AGI" without it losing the computer-like usefulness of an ML model.
- SemanticStrengh 4y ago
  We are on an AGI winter in NLU. Cool openAI demos are cool and irrelevant. The HN crowd should really learn to go see the leaderboards by himself on paperswithcode.com then he would realize the reality, we are stagnant on the key basic tasks (e.g coreference resolution). While GPT-3 is just a subtile bullshit generator that push to its paro(t)xism the illusion of understanding that mere collocated statistics amalgamation provide, Dalle-2 on the other hand is very impressive but it does not advance the key NLU tasks and just show how far a smart trick (constrastive learning) can go before plateau-ing. The idea consciousness emerge proportionately with the accuracy of your mental isomorphic world representation is cute however we don't become more conscious by becoming more erudite, and the most intense magical qualias, such as e.g orgasms are accessible to the simplest mammals and are unrelated to activities in the higher cognitive regions of the brain. Even a newborn that has no understanding of its surrounding experience qualias.

jimbokun 4y ago

I'm not sure engineering types will be satisfied with these kinds of conclusions: > “It is not someone else’s job to figure out the why or what happens when things go wrong. It is all of our responsibility and we can all be equipped to do it. Let’s get used to that. Let’s build up that muscle of being able to pause and ask those tough questions, even if we can’t identify a single answer at the end of a problem set,” Kaiser says. Hopefully the actual course content is more concrete than this. But this kind of language strikes me as encouraging people to feel the "correct" way about a problem, but not really emphasizing coming up with concrete, actionable solutions. And without actionable solutions, I feel like the value of this content would be very limited.

noasaservice 4y ago

Perhaps this is a hot take, but when I see ML being used (for the most part), it's because people do not fundamentally understand OR know how to solve the problem in question. And instead of understanding what's going on, and solving the problem efficiently, instead it's "throw GB's or TB's of data at an algo that we tweak and hope for the best". Sure, it gets results for massive processing times and data. And sure it's "fuzzy" and can fail on really stupid stuff and provide bad answers confidently. But it'll get the next VC funding line, won't it?

axg11 4y ago
Let's look at a few of the most impressive applications of machine learning: - Classification in computer vision - Protein folding (AlphaFold) - Image generation (Dall-E 2) - Answering general language queries (GPT-3) It's unclear how any of these applications could have been tackled _without_ machine learning. We had no solid grasp on these problems before ML, with the exception of protein folding. I think you are being cynical. Of course not every ML project will result in a home run success. Should that make us sceptical of the entire field?
- Jensson 4y ago
  But most people using neural nets in the industry aren't working on those things. They just slap it on any data inference task, even though in most cases traditional statistical/data science models works much better.
  - r-zip 4y ago
    In my experience, people use appropriate tools for the job. Most data scientists won't reach for a neural network if the problem can be solved by other, simpler means.
    - Jensson 4y ago
      Which is why I said "using neural nets" instead of "data scientist".

jasfi 4y ago

For those interested in NLU/AGI, see LxAGI: https://lxagi.com https://lxagi.com. No demo available just yet.

ShamelessC 4y ago
Looks like vaporware.
- jasfi 4y ago
  The MVP is being developed, just not yet ready for release. The landing page is for those who want to sign-up to be notified of news.