Hello, I'm going to tell you about witness and distinguishability for any single round argument with application status control, and this is joint work with Yale-Kali. Start by talking about delegating NP computation. So this is the setting where we have a verifier, which is usually computationally weak. And it wants to learn whether some NP statement X is valid or not, whether X belongs to some NP language L. And it wants to use the computational power of a powerful prover or server, which has the witness for this NP language. So the obvious way to do this is for the prover to simply just send the witness to the verifier. What we want is to do it with fewer resources in terms of communication and computation complexity for the verifier. So what we want is a proof system, which of course needs to have completeness and soundness, but also should have succinct communication. So the communication should be much lower than the length of the witness W. And also the complexity of the verifier should be much lower than the complexity of actually running the NP verification on the actual witness. Given these requirements, we can just hope to send a message from the prover to the verifier, but we still want to minimize the interaction or the number of rounds of communication. So what we can hope for is to have a two message protocol where the verifier first sends a query queue and then the prover responds with an answer A. And furthermore, we can hope that the query queue can be generated independently of the particular NP statement X that we want to verify. Another requirement is that the complexity of the prover should also be moderate. So it should be proportional to the complexity of actually verifying the NP witness with the statement. So this is in general the task of delegating NP computation. And we can think of a few variants of this task. For example, the question is whether the verifier needs secret information in order to verify the validity of the answer A. So we can think about public verifiability where everybody who has Q&A can actually run the verification procedure and check whether the verifier would accept or not. Or we can think about secret verifiability where the verifier, while generating the query queue, also generates a secret key which keeps secret from the prover. And the secret key is used in order to verify the answer A. And indeed many protocols in the standard model, you actually need this secret verifiability. Another parameter that we want to consider is selective versus adaptive soundness. So the question is whether the NP statement X can be chosen by the prover after seeing the query queue for soundness purposes. Or whether we can assume that the NP statement is chosen independently of the query queue. And last parameter that we're going to talk about is privacy for the prover. So the prover might want to also maintain the privacy of the witness that it is using. And in this communication pattern, this translates to the notion of witness indistinguishability. So the verifier should not be able to tell whether the prover used witness W1 or witness W2 for proving the validity of the NP statement. In terms of known constructions, so in the random oracle model or under knowledge assumptions, we can really get the best possible parameters and even get a single message argument system with optimal communication complexity and computation complexity and so on. In the standard model, things are more complicated than we only have limited results for limited classes, subclasses of NP statements. And in particular, these protocols require secretive variability. And what we do in this work is sort of try to enhance the properties of these NP delegation schemes. And in particular, we're going to show how to add witness indistinguishability generically to these types of arguments. So let's talk about our results in a little more detail. So what we show is that it's possible to generically transform every NP delegation scheme of this form into one that also has witness indistinguishability. So we can add privacy for the prover on top of an existing delegation scheme. The cost of this transformation is an additive increase in the communication complexity and the verifier complexity compared to the original delegation scheme that we started from. This additive factor is a polynomial in the computational complexity of the original verifier. So our new communication complexity and our new verifier complexity are going to be additively larger with the factor that's polynomial and verifier complexity. However, if we start from a very good delegation scheme where the verifier complexity is much smaller than the length of the witness, then this property is going to be maintained even after this additional transformation. In terms of assumptions, we require a super polynomially secure two-message maliciously secure oblivious transfer. And in addition, we require that the original delegation scheme that we start from is also super polynomially sound. And we're going to see where this comes in when we talk a little more about the details of our transformation. The other result in this work is an application. So we present a new primitive that we call an access control scheme. And I'm going to say more about what this primitive is later on. But if you know the notion of anonymous credentials, then access control schemes are similar to anonymous credentials. And they also offer succinctness for the credentials, which is sought after property in this context. However, they do not have anonymity against the issuer of the credentials. But I will say more about what these access control schemes are later on. In order to construct these access control schemes, we have our witness instability transformation. We want to apply it on top of one of those NP delegation schemes in the standard model that I mentioned in the previous slide. And for this purpose, we also need super polynomially secure single server private information retrieval scheme, because this would allow us to instantiate those NP delegation schemes for limited classes of NP statements. And this would give us the access control scheme that we need. So concretely, if you sort of take all of these required building blocks and sort of go down to the concrete assumptions that are needed, then our results can be instantiated based on the existence, based on the hardness, the super polynomial hardness of assumptions such as learning with errors, Decisional Diffie-Hellman, Decisional Composite Residuosity or Quadratic Residuosity. So these are the results. And what I'm going to do is start by talking about our generic witness indistinguishability transformation. So what we want is to start from a delegation scheme and add witness indistinguishability on top of it. So in the original delegation scheme, it is possible that some information about the witness leaks to the verifier through the response A that the prover needs to compute. And we want to prevent that. And the basic idea is rather than sending A in the clear, what we're going to do is send a commitment to A, and a proof that the committed value is sort of proper, is really a value that satisfies the verification procedure. And of course, this proof needs to be a witness indistinguishability proof. What do we win by this? Now the witness indistinguishability proof only needs to apply to a statement that has a short witness, because it's a statement about the value inside the commitment and not a statement about the original NP statement. And therefore, even if we start with a witness indistinguishability proof that is not succinct, the eventual communication complexity is going to be succinct. So let's do this in a little more detail. So instead of sending A, we're going to send a commitment to A using some randomness row and an additional proof. And what is this proof? So this is a witness indistinguishability proof that C is indeed a valid commitment to a value A, and this value A satisfies the verifier's predicate with the given query queue. So we want a witness indistinguishable proof of this statement. And this is of course a very simple and straightforward idea, and it indeed can be made to work. All we have to do is sort of take care of the subtleties that arise where you actually try to apply this outline, this blueprint. So in terms of soundness, in order to be able to prove soundness, what we need is to show that if we have a prover that succeeds in this new transform protocol, then we also have a prover that succeeds in the original protocol. And the way to do it is to try to sort of take the commitment and pull out the value A, and this is the value A that will allow a prover to succeed in the original protocol. So what we need is to be able to extract the value A out of the commitment. However, we don't have additional rounds in order to perform extractability. So we're going to use complexity leveraging. In particular, we're going to use a commitment scheme, which is sort of fairly weak. It's super nominally hard to break, but it can be broken with some super nominal computational complexity. And we're going to sort of use brute force in order to break the commitment and extract the value A and use it for the purposes of the reduction. And this is the reason why we actually need super polynomial soundness for the original delegation protocol and for the witness indistinguishable proof, because these need to remain sound even in a setting where we can brute force open the commitment. So this is where the super polynomial assumptions come from. So this is in terms of soundness, other complications that we need to resolve. First of all, as I said, in many cases, in particular, in the cases that we want to use for application, the protocol is only secretly verifiable. This means that the verify has some secret key that it uses in order to check whether the response is valid or not. And if the prover wants to come up with a proof that the verify would indeed accept, then they cannot do it because they don't have the secret key and they must not have the secret key, otherwise soundness is broken. So we need to handle this difficulty. The additional difficulty that we need to handle is that if we just use standard witness indistinguishable proof here, then we're going to run into trouble because if you look at the statement that we are proving here, it really only has one witness. The value A, since our commitment needs to be statistically or perfectly binding for the purposes of brute force extraction. It must be the case that there's only a single witness that the prover actually uses for the setting of this witness indistinguishable proof, and therefore witness indistinguishability is actually meaningless. So we'll need to figure out what to do in order to get a meaningful notion of witness indistinguishability even in this setting. Let's start with handling secret verifiability. So now the verifier also has a secret key that it uses in order to verify the response A. And the idea is to use fully homomorphic encryption. So I want to sort of allow the prover to approve a statement that relates to the secret key, but I don't want to give him the secret key to clear. So what I'm going to do is send homomorphic encryption of this secret verifiability key to the prover. So I'm going to send the query and the public key for the homomorphic encryption scheme and some C0 which is an encryption of this verifier key. Now the prover can evaluate under the encryption, the verifier's predicate, and it can obtain an encryption of what the verifier would have output given the value A that the prover generates. So let's call this encryption CT. So this is supposed to be an encryption of one if indeed the response A is a valid response for the delegation protocol because the verifier is supposed to accept the prover's response. Now, what the prover is going to send back is it's going to send this ciphertext which is supposed to be an encryption of one. In addition, it's going to send a proof that the ciphertext was indeed generated properly so that it was generated by performing the homomorphic evaluation on the FHE encryption of the secret verification key with a response A that is committed to in the commitment C. So this is now going to be the statement that is going to be proven in WI and the verifier can now check that this ciphertext can check first of all the validity of the WI proof. In addition, it can check that indeed the ciphertext CT decrypts to one, which sort of guarantees that the ciphertext CT is indeed a homomorphic evaluation of some response that is committed to, and this response actually leads to, would have led to the verifier actually accepting. And this is sort of our strategy to deal with the secret verifiability issue, but we noticed that what we need is a homomorphic encryption with a circuit privacy property because we wouldn't want information about W or about a to leak from the ciphertext CT. So we need this circuit privacy property, which means that the output ciphertext does not leak information about the computation that was being performed. And furthermore, we need malicious circuit privacy because the parameters of the homomorphic encryption are actually generated by the verifier, which we want to protect against. Indeed, we can get malicious circuit private fully homomorphic encryption under fairly mild variants of the learning with errors problem. So this is one way to resolve this issue, but we can observe that actually we need less than fully homomorphic encryption for this task. In fact, the compactness property of the fully homomorphic encryption may not be necessary in this setting. Compactness means that in our case that the length of the ciphertext CT is independent of the size of the circuit for which we did homomorphic evaluation. But in our case, the homomorphic evaluation is of the original verifier. So the complexity of the original verifier is actually bounded and it's actually possible that other parameters in the scheme are also at least as large as this complexity. So even if we don't have compactness for our fully homomorphic encryption scheme, we get the same guarantee about the length of the prover's response in the worst case. So we can actually rely on a variant on something similar to fully homomorphic encryption without compactness, but with malicious circuit privacy. And this can be constructed using garbled circuits and maliciously secure oblivious transfer. And this we can do other additional assumptions. So in addition to learning with errors, we can also do it under DDH, Decisional Composite Residuosity, Corridor Residuosity. So this finishes the part about the secret verifier. Now let's talk about the witness and distinguishable proof system that we need. So first of all, let's notice that we want the witness and distinguishable proof to have adaptive soundness. Remember that we talked about adaptive soundness. And I should emphasize that the original delegation scheme that we start from does not need to be adaptively sound. And furthermore, the delegation scheme that we end up with, the one that has witness and distinguishability, is also not going to be adaptively sound. And nevertheless, we need to require that the witness and distinguishable proof system that we use inside our construction does have adaptive soundness. And the reason is that the statement that is proven using this WIProof system is only determined after the prover has seen the first message of the verifier. So in fact, the statement is chosen sort of adaptively after the parameters for the witness and distinguishable proof have been selected. And therefore we need adaptive soundness. So this is one requirement that we need. The other requirement is with respect to the variant of witness and distinguishability that is required. And this is what I mentioned when we talked about the obstacles. So just plain witness and distinguishability is not going to be good enough. What we need is this notion of strong witness and distinguishability, which talks about witness and distinguishability between distributions of instances and witnesses. So in strong witness and distinguishability, we consider two different distributions of pairs of instance witness. And what we say is that if the distributions of the instances are computationally indistinguishable, then it should also hold that if we generate proofs with respect to these instances, then the proofs themselves are also going to be indistinguishable. So you don't just hide which witness you use for a specific instance. You also hide which instance you're using when the instances come from two computationally indistinguishable distributions. And indeed, it's going to be the case that in our setting, if the prover starts from two different witnesses for the original NP statement, W1 and W2, this is going to translate to two different distributions on instances, which are computationally indistinguishable. And therefore, this notion of strong witness and distinguishability is actually going to provide the type of witness and distinguishability that we need in order to argue that the final witness and distinguishable delegation scheme that we have does have sort of the standard notion of witness and distinguishability with respect to witnesses for the original NP statement. And in order to, in order to actually instantiate this, this component, we use the words of Jane, Kalei, Korana and Rothblum or Kalei, Korana and Sahai, and they construct strong witness and distinguishability for NP under polynomially secure malicious oblivious transfer, which exists under learning with errors, DDH, Decisional Composite Residuality or QR, so all these assumptions that we that we actually could rely on. So this concludes our witness and distinguishability transfer and let me briefly talk about our access control scheme. So let's see what is access control. So we consider a space of attributes so it could be a very large space. Let's just, we're just going to number the attributes with numbers between one and capital M. And what we want is for an authority to allow an authority to assign credentials to a user, which corresponds to a subset of the of the attributes. So each user has a subset owns a subset of these attributes. And we have an authority which has a master secret key and of course there's a master publicly that everyone has access to and use the master secret key. It can give credentials to a user to a user which sort of prove that the user has indeed owns these owns these credentials. Once the user has these credentials, then any entity can efficiently sort of challenge the user and check that the user actually has attributes that satisfy some monotone relation F. So the monotonicity here is because well, you know, if you have a subset of the attributes, then of course you also have some smaller subset of the same attribute. So we only need to deal with monotone monotone relations here. And this procedure can be done efficiently and succinctly. As we will see in as we will see a minute so we want to be able to allow any entity in the world to issue a query such that then the query contains some monotone relation and the user who has credentials that satisfy this relation. And it's going to be able to show that it indeed owns credentials that satisfy the relation, but without revealing any additional information. So, as I said we want soundness and collusion resistance. So these are the standard notions for this for this sort of attribute based schemes. We want succinctness so we want to we want the query and the proof to be to have communication and computational complexity that are much much smaller than the size of the than the size of the relation. So the relation could have like sort of many, many different clauses it can be so either you have attribute one, or you have one of attributes, you know, two to five, or you have attribute six, or you have attributes, 18 and 19, and so on. So this is a sort of a very long monotone relation, but we want the query and the proof to be independent of the complexity of computing the relation. We want anonymity in the sense that the in the sense that the verify the person who issues the cues and the query and gets the response cannot tell which cannot tell which attributes the user actually has. It only knows that the user has some set of attributes that satisfies the monotone relation. So in order to construct this we use as a building block, this notion of batch NP delegation. And let me explain why that is. So, let's start with a notion of the batch statement. So about statement is a collection of sort of small empty statements, and in additional an aggregator function that computes the compute a predicate on the validity of these NP statements. So let's sort of look at a drawing. So we have a bunch of these small NP statements x1, x2 up to xm. And each one of these NP statements, these inputs can either be in a language L or can not belong to the language L. And on top of this, and on top of these sort of predicates, so each one of these predicates you can just think about as a bit. So it's one if x is in the language and zero if x is not in the language. And there's a function f that is applied to this collection of bits. And this constitutes sort of the total batch statement.  And on top of those, you compute some, you compute some relation. And these works that I mentioned actually allow to actually allow to provide succinct delegation schemes for batch NP statements, where the verifier complexity and the communication complexity only scale with the length of the small NP witness and not with the length.  However, it's possible to show that you can do you can actually get delegation schemes, where the communication complexity only scales with the length of a single witness for these for these small statements. And this is what we're going to use in our in our construction of the access control scheme. So our master secret key and master public key are just going to correspond to a standard signature scheme. So there's going to be a verification key for signature scheme uses master public key and assigning key uses a secret key. And in order to assign the credentials you simply are you're simply going to sign the attributes that belong to the user together with a tag that corresponds to the identity of the specific user. So this is how you assign the credentials. Now, in order to successfully to succinctly prove that you own credential satisfying the relation F, you're just going to use a batch delegation protocol. So you're going to use these batch delegation schemes that we that we just described and indeed you can have batch delegation schemes where the aggregator function is a monotone formula. And you're going to be able to provide a proof to issue queries and then provide 16 proofs for sort of the validity for owning credentials that satisfy this that satisfy this batch NP relation. And the success property means that you're only going to the length, the communication complexity is only going to scale with essentially the length of a signature, which is just going to be a polynomial in the security parameter. Now in order to get anonymity, we use our witness extinguishability transformation on top of what I described so far. And this is going to give us anonymity it's going to give us witness extinguishability for this delegation scheme, which actually means that the the verifier cannot tell which witness was used in order to prove the validity of the statement, which actually means that it cannot tell which attributes the user was the user actually owns it only knows that these are the own some attributes that satisfy the relation. And this concludes the construction, and this also concludes my talk. Thank you very much.


好 女士們 先生們 各位貴賓我們會繼續在這一個 forum 中我們在世界中 會有不同的發展我們會在這一個 forum 中會有不同的發展我們會在這一個 forum 中會有不同的發展我們會在這一個 forum 中會有不同的發展我們會在這一個 forum 中會有不同的發展我們會在這一個 forum 中會有不同的發展我們會在這一個 forum 中會有不同的發展我們會在這一個 forum 中會有不同的發展我們會在這一個 forum 中會有不同的發展我們會在這一個 forum 中會有不同的發展我們會在這一個 forum 中會有不同的發展我們會在這一個 forum 中會有不同的發展我們會在這一個 forum 中會有不同的發展我們會在這一個 forum 中會有不同的發展我們會在這一個 forum 中會有不同的發展他們會介紹給我們什麼樣的未來世界會變得相似不同的人會告訴我們未來世界會變得完全不同從今天起所以我今天很高興有這些 panelists with me today首先,我們有教授邱安娜布萊森,從巴斯大學的大學和學校的大學接下來是副總統和副教授蜘蛛公司的副教授劉浸仁第三位是劉浸仁在中國的新副教授還有邱奕奕從基督教授的學校我們也歡迎你們還有什麼樣的我們也有副總統卡朱拉副總統阿斯托利亞以及在下一段時間我們會在下一段時間五到十年會在這段時間會在這段時間會在這段時間會在這段時間我們會在這段時間什麼樣的一刻可以告訴我們嗎我們會有You have your specialty fields, and my first question is from Mr. Liu, Liu Jiren.You are the first generation of doctor in computer science, and you represent new software, a computer science company.So you are one of the artist who was in touch with AI, and from your first hand experience, what kind of stage we are in now.Also, what will be the next few years by looking at the technology, by looking at the past decades.We really set up a digitalized world with later foundation and infrastructure and platforms.Number one, the computing cost is being driven down.Once it was very costly, now computing cost and storage has become cheaper and cheaper.Telecommunication has become cheaper, and these kind of cheaper are still in continuum.And also digital equipment or device has become everyone's device put together.We got to see this world connectivity has become faster, convenient and cheaper.And therefore, on the basis of this, we see more of the multiple applications, which made this world different.We see e-commerce, e-learning, we got to see different kinds of social media, the platforms, and the connection between people and objects.So real-time cheaper and cheaper, actually.And this led to some major breakthroughs, like in AI.When I was a university student, I read about AI and also virtual reality, and we see upheavals, ups and downs.But why AI has not been turned into a reality?Because we do not have a foundation or infrastructure in place.However, nowadays, we can do computing to do weather prediction forecast in an instant.But once, it took two days to predict the weather, and the storage also has become cheaper and cheaper.Another major change is the extraction of data has become more proactive and cheaper as well.And once, when you want to get data, you have to purchase the data.But now, you have free data.People are contributing data even about their privacy, their track record behavior, their lifestyle.They are contributing all these.And in digital society, you have more accuracy in identifying different individuals, their behavior.All these changes in discussing AI, the foundation is totally different today than yesterday.And once, we talked about what kind of algorithm.And once, we thought the algorithm was not speed enough, but the speed is not a problem.And once, you did not have enough preconditions like data information, but you got sufficient data information.And so, this is unprecedented.And what is AI?And what kind of legal status should it have and ethical issues surrounding AI?This is something we never experienced before.And so, because these data has given us a totally new world.And now worldwide, we see more and more population living in another space and their commercial behavior, governance.And it's totally different from yesterday because of the differences.So, they turned into economies, they turned into cultures.They turned into some major challenges and issues to discuss.Talking about the next 5 to 10 years, what will be the biggest major change?We will see, not to talk about more loss accumulation, cheaper and better, faster.Certainly, this is something we will see.But in the next 10 years, after 10 years, our understanding about the whole world will be more accurate.And the relationship between different objects, we will have a clearer picture and put together.And if we come up with a new algorithm, then regarding every sector today,we'll be talking about internet and people and their connection with people.Now, all these sectors, technology has become a disruptor or promoter of the internet.So, in the next 10 years, we will get to see something we never got to see in the past 20 years or 30 years.It will come faster and faster because of digitalization and better accuracy and more business models,new business models as well.China, for example, on these kind of platforms,more and more Chinese companies are coming into being and their productivity is being enhanced.And entrepreneurs of startups, they are becoming younger and younger.They accumulate wealth more quickly and this is something we never experienced.Especially those who do not have any capital only got a brain power and they could have a startup.And this is something we will get to see in the next 20 years, 10 years,digital startups to accumulate wealth and to come up with new business models.I call it something new normal.Now, I have a question for Joanna.You started in the 80s.You got involved in AI, computers.What kind of...Sorry, he was translate you for a while.Sorry.I mean, you have started your research concerning to computer and AI since 80s.So, that's a long time.So, I really want to know your feeling about those biggest change compared to those timesand what is the most exciting thing lying ahead.Oh, wow.Let's see.So, I think the biggest change is not because of artificial intelligence itself.It's not the algorithms, but has been, as everybody says,the expanding of the computers, the expanding of the internet.And then, of course, since 2007, the expanding of the personal data.So, it isn't been the details of AI that's changed.But in the last two or three years,what's been changing radically is the governance.It went from being toys to changing the world.And we've seen it change in democraciesthat there's been a challenge to democracy.And so, now people pay attention.Also, there's the change both on both sides of the Great Firewall.There has been a change of the tech companies becoming more powerfulthan petrochemical and then manufacturing.So, that's a big change.It's a big change in power structures.So, I think it isn't...And I loved your video,but I don't entirely agree with it.It's not that artificial intelligence is a bunch of people.There was a focus on these robots.Artificial intelligence is a means by which humansget control and information over their world, including each other.And so, I think the challenges,the biggest challenges going forwardthat we're spending a lot of time onis how do we handle the problems of accountability?I agree.I don't think...I actually don't think we need a lot of new laws.I think what we need is to help people to enforce the old lawsand to hold accountable companies that were not doing the correct thingswith respect to being careful.So, some companies,for example,Facebook became very powerful,maybe even not entirely intentionally,and then they weren't following the best business practice.And if the courts find them incorrect,then they should be held accountable.So, I think that we're making companiesare incredibly powerful.They're very rich.They're transnational.We need to figure out new ways to negotiate with that.I don't think that was really the question you asked me.So, just to say,the technology and before this sessionI had thought this when I first heard someone sayI would be most excited if we could do live translationand everyone everywhere talks in their own language.Like we have here,but we use people.That will.I think I keep expecting at one of these meetingswe're all going to have these headphonesand it's going to be...and it won't be as good as the people.We can put something here.Well,no,because it is still...We won't get the idioms.We won't understand all the pieces.It's still worth learning another language.But it will be like Google translate online.At least we could get the words and kind of guessif we know enough about the culture.So,I would love to have everybody speakin their native language all the time.That would be great.We're looking forward to it.OK.想請問我們周奎先生.I want to ask you,Mr.周奎,very famous startup company founderand also the investors.Would you please to share with meyour view?What are the fundamental or the most importantchange we are experiencing?And in your vision in the future,of course you will select some major changeshave great impact on the companies,on the industries.Well,we are in the process of informatization.Technology is a big step of humanity's civilization.And everything could be digitalized.We could stay connected.We can pull things together for algorithmand computation.And a lot of things are enabled.The symbol of the civilization is savedat cloud.Yesterday,in Shanghai,we invited Amazon,Tensant Google.We said that the people wisdom could be calculatedand computed and saved at cloud.Such kind of wisdom.In China,we say that elderly seasonsare most wise people.But the cloud stays young forever.And the brain of the cloud is expandingand exceeding the individual capacityof a single brain.AlphaGo is one of the milestonesand the chance is so difficult.And I believe it will have a lot ofchallenge to people.We need to get used to many changes.The top wisdom in the society could beaccessed by everyone at cloud.Maybe it's not today.We already see such a progress,maybe some of your personal lifeand your career choice,your personal choice,your career choice need to getadaptive to such a change.I believe these are the changeswe are having now.And we can form such a structure.We may have a very,verysuper social brain in the cloudwhich is able to support everyone.Well,I think that individual personcan already get tech wisdom,which means that the mobile phones,you can do it on your mobile phone,which cannot be imagined in the past.I'm able to see my location,the accurate location,which was you cannot imaginetwenty years ago.I believe this is what we have nowadaysand in the wisdom of theinformatization is growing quickly.People believe that connectivityis growing quickly.It's also the accumulated wisdomare also growing.Well,within your own vision,which industry or sectorwill enjoy the fastest growth?I think it's the industrystill in difficulties.Whenever we talk aboutthe technological revolution,we often say it wasthe change of mobility.We know that the mobilityis one of the highest costof human livesand mobility,transportation,autonomous driving.Five years ago,I said it was not difficult,you know nowadays,I think it's much less time.We already have some licensefor the autonomous driving.Maybe in the next five years,I could even useautonomous driving vehiclesand you can try it.In the next five years or lessin some regional range,transportation or mobilityis one of the topics of people's lifeand which is usually cost some money.Healthcare is difficult to educatea student to become a doctorand the doctor has toreceive education in the pasteight to nine years beforehe is qualified as doctor.But such a medical informationand knowledge could beaccumulatedand used bythe machines.When this brainor the machine brainis able to help you,you know that we can have fastertraining of the doctorswhen providing treatmentto the patientsand they are more accurateand they will have lessmistakes made.But you know this is a rising partof the business just started.The other industry or sectorstake a securityas an example.You know that we have vulnerabilitiesand the machine brainhas less vulnerabilities.It is ableto identify everythingin its memory.Why notI use it to servefor the security purpose.Of courseit brings new problems and challenges.The other thingsChina is a large manufacturingcountry.Why?Because China has a large populationbase.And I usedto work within a plant.There are so many assembly lineand the testing line.In assembly lineyou can do the assemblyby the machines.But to testwe have visual inspectionat the plant.Humansare slowerthan the machines in the visualinspection.Not onlythe speed but also the quality.For example,theapple mobile phoneand it is not.The jobis not done by people.It must bedone by the machines.There are a lot ofsectors and industries.And we will see penetrationofsuch a technology.Anywaythe companies will be drivenpartially by AI.People sayAI is not a specialindustry but it will go intoany kind of industry.I knowthat Mr. Xiao,you areand I know that you are oneof the pioneers in AIin combinationwith internet.Mr. Joetalked about security.Probablyyou have more things to say on that.In terms of finance or othersectors,what do you thinksome surprising changes you wantto tell us?In the past severalyears,we dosee radical changes in theworld every day.Peoplefeel depressionin fear ofthe possible changesor replacement by AI.You know that Chinesepeople are very smart andwork diligently and wedevelop practical science and technology.The application of AIin China,at thepresent,whichwe see very clearly,whichcan create most value withinthe commercial use,isthe advertisement algorithm,theadvertisement push up.Theother thing is that in thenew finance,it alreadycreate new value.We do seethe profit driven by AI.Andautonomous driving,whichis still on the experiment,whichis not ready,but in financewe already have it.In terms of finance,andthere is a concept.Youknow that in China,whichis the earliest industry,whichisdigitalized.Thatis a bank,orinfantamanization.In thepast,we had large machines,largecomputers.In financialsector,they have a very goodstorage of data,buthowto use the data.Howto apply data within thefinancial sector,which waspast,in the past.China's population,billion,1.4billion population.Youknow the supply and demand isnot in right balance,especiallywhen providing financial service tothe individual in China.Theintegrity system in China isnot quite sound,differentfrom US,which has a lot offacult data.There is aninadequate of integrity,orthe accredited rating dataof that in China.Oncethe AI penetrated intofinance,can create a lot ofvalue.The first one is interms of antifraud,andthe background check.Andthe result ofusing AI in financialindustry can drive down yourcost,which means bringmoney to you.Theother thing in the small loanoperation,you know thatusually in traditionalindustry,why they do notinvigial?Becausethey have a lot of costandthey have alot of cost of application forclimbing back the loan.Andall office could be addressed bythe AI solution.Inthe past years,we followedclosely with the technologyhow to implement thetechnology.It is truethat from the customeracquisition and how topick the right customer.Forexample,what wecan offer to themfinancially.Andalso we use some AIrecommendation algorithm toour customers.Theother thing is the approvalbefore the loan and antifraud.Wetalked so much aboutthe data generated atthe terminals,which canhelp us to identifysome small risks ofa fraud.I stillremember several years ago,wefind out one case ofa fraud.Inthe past,we can only identifythe fraud byhundreds of thousand ofexample.But now,somesmall possibility of fraud couldbedetected by several casesbecause the machines haveself-learning,checkingthe orders and thelocation of the transactionswhich can greatly preventand in ourapproval,90%of them are automaticwithout human interference.Postloan managementwithin our systemand ourorganization is not very large,maybehundreds of a billionassets under our management.Whatis the quality of the asset?Thenew asset fromlast month,how muchpercentage is good,how muchwe are not working.Youknow that there are6000 variables.Thereare so many variables,howto make the analysis by thepeople.We have machine learnedautomatically.They can identifythe MPL.We are able tooptimize the models so thatin the post-loan management,wecan accurately identify therisk to make our AUMin a healthy way.The otherthing is the claiming back ofthe loan.And we alsohave AI technology.And wecan lower our cost.And wecan make our financial servicemore inclusive within theindustry.Therefore,infinancial sector,AI has onesingle application,but alreadya good symbol showing that thewhole industry chain alreadyreceived the benefits.I believethat in the coming days,withmore practical application ofAI,we will see efficiencygrows in the society.Themultiply,we will havelower cost.You havetwo products,one is calledego AI,which is veryinteresting.Another expertat data,your companyis very popular now.It'sanalyzing,collecting dataand help other companies tomake better choice,the smartchoice.So what'syour opinion,yourperspective of what kind ofmoment we're in and whatchanged lying ahead?So Ithink like all the otherpanelists said,we're in thevery early days right now ofwhat's possible,as muchexcitement as there is arounddigital transformation andIoT,the number ofprocesses that truly arecensored end to end,andthe ability to collect thatdata is still really earlyand nascent.When I lookforward,I think for allof us to have a healthy societythere's always going to be risksand benefits to a new technologywith every progresscomes risks and benefits,andAI and machine learning have gotthat same edge of good and badbut part of the benefit should behow do we expose as manypeople to be able to participatein the way of this comingand what we seewith the many organizationswe're working with,there's threeaspects that I thinkwill help a broaderbased benefit.The firstis open access todata.We've all been talkingaboutdata on the panel,andI've viewed data as the oxygenfor machine learning or for AI.It'sthe backbone,it's thefuel,it's the capabilitythat we will all need tocontinue to progress ouroverall attempts at machinelearning and artificial intelligence.Andthere's a lot of data becomingavailable as everything becomescensored,but I think we have tostrike a healthy balance betweenanonymity of data and protectingrights and still making dataavailable so we can see the gainsin health care,in retailand financial services that arepossible if we really have accessto massive broadbaseof nonstructured and structureddata.I think hand inhand with that accessis the trustworthiness of data.Weall hear reports of manipulationthat's now possible in manydifferent fronts.How do youand not just data but then obviouslythe algorithms that operate on thedata given that there's a verytight virtuous circle betweenapplication different algorithms andthe data sets that you're manipulating.Howdo you get visibility and trust onthe lineage of data.Obviouslyblockchain and distributed ledger canbeone of those aspects,butyou can't trust the foundationof the element that you'reworking with or you don't knowwhere it's been and how it'sbeen enhanced in some way.Ithink that starts to minimizeagain the potential for societalgain,but also could takeai and tracks that wewouldn't always understand withthat transparency.Then thethird piece that I think isabsolutely critical that lots ofpeople have been talking about isthe ability set so that morepeople can participate.So wecan democratize data,democritizemachine learning so that theaverage citizen that's not adata scientist that may nothave been trained in verydeep computing capabilitycan still benefit.And Ithink we're seeing a lot oftooling if we can get accessopen and we can get trustworthyand transparency high.Ithinkwe're seeing a lot of toolingto both represent algorithmsand data sets in ways thatare story processing brainwhich is not a logicprocessing brain throughevolution in ways that ourstory processing brain canactually attached to.Andif we do it properlyIthink the ingredient thathumans can continue to rely onis curiosity.Wheregeneral purpose AI stillis a long way to go isnon sequitur linking ofdifferent patterns and thecuriosity to really investigateand interrogate those patterns.Sowith open access,trustworthiness,training,enhancedcuriosity and accessibilityof that data,thenthere will Ithink there will becontinued movement overallfor this fourth industrialrevolution,but ideally inmore positive ways thannegative ways as we getanimationandthe benefits that cancome with that.Possitivethings and negative thingwe're going to face ittogether.We have nochoice.Wehave no choice.Sofrom the datause perspective we'restill in the early stage.Iread a report which sayscallin to maturity86% of the AI technology has not got into maturity.And even by 2022, even 40% would not be mature, get into mainstream market.But 85% of the technology will bring transformational opportunities for different sectors and bring lots of values.So we are still on the way.And I have a question for Mr. Liu, Liu Jiren.And regarding the sectoral changes, once we call it, it's like oxygen, data is like oxygen.And algorithm we have put together will be a new engine.And to make, to upgrade the different industries, different sectors.And but for some sectors, this could be disruptions.So my question for you is about looking at the changes to different industries to society.So what kind of forecast prediction you have?I think these changes will be huge.Let me take health care as example.Health care or medicine in any country in the past decades or even 100 years of history.We intended to give our people better health care, better welfare.But no country has been able to hold up the burden because our payers and service providers are separated, are severed.And patients and medical service providers are severed, are separated.And we lack transparency.We cannot ask a doctor why you give me this injection.Why did you prescribe this medicine?And also in different cities, we also see a disparity in health care in medicine.And therefore the conclusion is that we will see a continued increase in the health care burden in different countries.But they are related to how to optimize our medical care system.Firstly, we see lots of separations, not very good connectivity because we have no good handle on data.And anyone in any city would not be able to get to know which hospital is the right one for you, which one is the most affordable for you.You do not know.You just know which one is the best.So we always go to the best hospitals.But the best one doesn't mean that's the right hospital for you.They may not have the doctors to treat you.And they could be very costly.And in this kind of background, so AI in the future certainly will play its role in medical care.I think there are several forms of the roles to be played by AI.Firstly, we have different objectives.And that is how to beat the medical doctors.But that's not our objective anymore.We want to turn the worst doctor into the best.And we do not want just AI to beat the best doctors.And if we can turn the worst doctors, turn them into best doctors,only by 5% they will give us more benefits to the society.Because most of us, 70 to 80% of us,our diseases are common ailments and chronic diseasesor treatable diseases or curable diseases.But you do not get cured.You do not get the right diagnosis.It doesn't mean that by going to visit the best doctors,you would get treated or cured.That's not the picture today.And also in China, we have the regional disparities at the county level.Many medical doctors even do not hold a medical degree.And we have a farmer-to-farm treatment.We call it bare-footed doctors in China.And so in medical care,AI, if we can turn the worst doctors into the best ones,if we can turn the best doctors into many more,they can turn the methodology into algorithm.Their medical plan can be connected to other doctors.So the prescription can become best practices to go to the most remote areasto treat people there.And I think this is something we need to pay attention to,but we have a long way to go.And from China's perspective in medical care,firstly,our population is beyond their affordability.And because of the diagnosis,it has got a problem.It's not just the drugs are expensive.The most expensive part is the diagnosis.You got the wrong diagnosis,and then you were prescribed the wrong medicines.That makes your treatment very expensive.Secondly,your disease could be treated in a village at the county level,but you took a train to Beijing,and to wait in Beijing for one month or two months.And that made your medical care very expensivebecause there is a lack of trust to hospitals.You have to move to bigger cities to get treatment.Therefore,in medical care,if you do not go beyond medical care,if you just want to adopt some reform measuresto ask the doctor to be cheaper,to ask the hospital to be cheaper,you cannot solve the problembecause our doctors are like artists.Doctors are artists.They are artists.So you have an eye on a doctor,you just want to go to him,but we need artists,but we also need engineers of doctors.Engineers can deal with the most basic problems,and the payment can also make some changes through AI.I think AI can play the biggest roleand there is the biggest possibilityfor AI to play a role in medical care.And for the society,we can feel the benefits very easilyif AI is deployed in medical care.So Mr. Joe,talking about the economic structure changes,what kind of disruptions we will witness in the near future.Mr. Liu talked about medical care.So which sector would be transformed thoroughly by AI?I quite agree with what Mr. Liu said.It's about inclusiveness.I mentioned that AI is to be usedby all of us.And other sectors also have these kind of features.For example,medical doctors,teachers,education.I think that would be a big sector for AI.Between teachers and students,they may give students assignmentsjust like doctors,but we require skillson the part of professors.But communication is one-to-oneon a small scale.And AI,regarding knowledge,per se,regarding the teaching of knowledge.For example,my personal experienceis one teacher teaching a dozen of usand 90% of my homework were correctand 10% were mistaken.But I spent a lot of time on the correct items.But we put students into batchments.We can ensure the most basic quality.But in the future,we will have a tailor-made teaching education.There won't be luxury as well.And tailor-made won't be just for luxuries.And teaching as a process.In the teaching process,you will face with different kinds of mishaps,unexpected events.But a machine can do management on the mishaps.And after they discover the mishaps,the people will step in.And when machines got involvedwhen there are mishaps,they send off a signaland the human beings will step in.And this can happen in many sectors.And AI can discover mishapsin the teaching processfor the kids started to cry.And then the machine will send out a signaland the teacher will come over.And so this kind of thing will happen in many sectors.So I call it major changes.So Joanna.Industry or social order,social structure.Oh wow,thank you for that question.That's one of my favorite questions right now.I think this is really a huge issue.And I want to say in the first place,I don't want to make an open predictionbecause what we do matters.And that's why I travel.It really matters that we get engagedand we use our brainsand we try to build a better future.But specifically,I almost want to take the question back to Professor Liu.When you say make the worst doctorsthe best doctors,that sounds great.I get really excited.That's why I got into AI.But then because I've been learningabout economics,I know that meanseveryone can be a doctor.Does that mean a doctoris no longer a prestigious position?So one of the thingsthat we're worried aboutis artificial intelligenceaffecting inequality.And I know that this questionis very differentin a traditional capitalistversus a traditional communist society.But it seems that social coherencecomes from the abilityof people to move between jobsand to have trustso that there isn't justan upper echelonand a lower echelontotally separate from each other.And that's a continuum.And I'm not sayingthis is not a challenge.It's a question.I sincerely wonderwhat all the consequences arealthough I'm working for them too.But this is a big, big issue.The second part of itis I mentioned beforethe transnational questionwhen there is one big winnerand again China isfamously the exception.That's why I mentioned the firewallthat they have been ableto createa tech sectorhere separatelyhas been really interesting.But in generalit tendsfor the rest of usoutside of Chinawe haveall the big companiesin one countryand they're gettinga lot of wealth.And even in Chinaa lot of wealthis going tothe mirror companieslike Tencent.So how you handlethat in your economyis a big questionespecially withinone countrythe government can workbut outsidewhen it's multi-companiesmultiple countriesthen there'swho is the government then?how do you agreehow to deal with that wealth?And then finallythere was another pieceI wanted to sayof the economic socialconc...Oh,finallythe AI itselfand there's beena little mention of this.It shouldn't be the thingthat hasresponsibility.So againthis goes backto the person thingbut people talkabout makingA.I. a legal personso thatthe driverless carfor examplehas its own liabilities.It's aconvenient thingthere's calleda legal personlike a company.And so whenProfessor Liu saidthere was no precedent.Some peoplesay companiesare the precedentthat they're a formof AI.And whena company is notwhen the peoplewho own the companydon't really carewhat happens to itthis is calleda shell company.And whenwe have shell companiesthen we have corruptionand thatI was very excitedby what you saidabout thelooking for fraud.So you cancreate fraudyou can saybuild a buildingdeliberatelyso thatit goes bankruptso thatyou hide moneycirculation.Ifno oneno humanso justiceworks byapplyingfor examplejailor aloss of faceto a humanthe machinedoesn't careand we cannotmake the machine carein a waythat wecan't un-makebecause webuild the machineit's notlike a personso with uswe are builteven fishare builtto carewhat thesociety thinksso we carethelegal personhoodwith humansand if anythingwe haveover extended italready that'swhy we havethe shell companyso I thinkthat's a veryessential partto theeconomicand social order我覺得這個有意思的就是說我this isvery interestingand wedo seesome manyexciting changesand theseriesofconsequencesminesand godown togetherand that'swhy it'shighly significantto discuss themwe need toget preparedfor welcomethese changesI wantto askMr.Xiaoand Joannahavetalkedso many thingsand Icanknowit'selaborateall of themand thesearegreat problemsandwe needpeoplepeople oftentalk aboutemploymentstructuresand it isa trendthat'sand how couldpeople becomeAI-drivenpeopleyou knowcompaniescouldbecomeAI-drivencompaniesand thepeople mightbefaced outbyAIand howto helpthem tofindthis isone ofthe trendpeopleyouyou havethe worriestoo earlyand even thereplacementtakes placeit takes timeandhistoricallyjobsweredisappearingandbut new jobsshowing upin the pastwe hadno programmerwe haveno productmanagersand whenwe arefacingandthe changewill notbethat suddeneven thoughthe societyisinfast changingbut itisstill aslow penetrationand youknowthatpeople arethespeciesmostor highlyadaptivetothe worldin theearthin thelong termand thethereareso muchsharepopulationmaybe peoplecan takeoffcan haveholidaysone afteranotherand wetaketermiststo goon holidaysand whatdo weneedand ififthereisa AIhelp usto haveour dailylifefamilywe candevelopourinterestwe don'tneed toinvest ourtime in theresourcesto do somemechanicalorreparative work,routine worklikecleanupand cookingand Ithinkthatwe cando morethingsand interestingthingsand thechangesandwhatwe needto haveorwhat kindofthingswe needto learnand youneed tofollowour owninterestto getadaptiveto thesocial changesand it'snotthatso youdo somereparativeworkand it istrueit's verylikelyadaptiveandpreparedI'ddatato companyand thenhelp themmake thesmartchoicepositiondo westill needentrepreneuryou cantellthe data willtell themhow todo thatdata isnotintelligencedata isdataso they'restillgoing backin the1800sif you tolda farmerthe world isgoing to go90%nonagricultureand there'smachines thatwill do everythingthat you do todaybutsomehowwe'regoing tostill haveglobalunemploymentunder 10%I thinkvery few peoplewould haverushed forwardand saidfantasticI can'tcan'tno-showeconomic changesare notsmoothlytaken upand what can we dothis timeto get in front of itwhat can we doonskill developmentand rapid retrainingbecause we willlose jobsthosereparative jobsthat theindustrial revolutioncreatedwilllikely go awayand we needto helpourpopulation becomewe needthe rightget intonew areasthat theywouldn't haveimagined beforebutI thinkfrom a societyperspectivewe have toshift resourcesto thatI don't thinkthat10 years agowe would haveimagined thatthere would bebillions of applicationson theitunesandroidstoresthat peoplearemaking someeconomic livingfrombutthatstill requiredagainthe oxygenfor thatsoI'll go backto my core pointonhow do wemake dataas accessibleas possibleand I'llground it with an examplethere's a group calleda global emancipationnetworkthat's workingon adifficult problemhuman traffickinggloballyit would have been impossibleto have solved15 and 20 yearsI think for a numberof reasonsone of whichis data was notvery open15 and 20 yearsdatabut if you cantap intothe hundreds orthousandsof sourcesacross travelindustryretailindustrydifferentlocalagenciespolice agenciesand otherspatterns canemergevery quicklythat showwho the victimsmight beand whothe perpetratorsmight beso you canactually beproactivein tryingtoanonymityneedswith thataccess of databutI thinkif we look at the pastand we're willingto lean forwardAIand MLis cominghow do weparticipatein itthen let'sfocus back onwhat do wehave to doto make surethat the broadestnumber of peoplecan benefitthroughthrough open datathroughtransparencyand trustworthinessand throughthethe dividethat I think iscausing a lot ofnegative ramificationsaround the globetodayas people getnervous and tryand do whatwe can never dowhich is tryand go backwardyou can'teven stand stillthe world is movingso how do youmovewith that worldgiven thatit's continuously evolvingwe have no choicewe have no choice刘先生您觉得如果Mr.you know thatsowhat do youthinkwhat should youteach to yourkeyswhat willbe still beused in thenext 30 to 40 yearswhat are the skillsand thecapabilitieswe need to graspI thinkanythingemotionalemotional exchangesI believesuch kind ofmajoror skillsare highlysecuredfor exampleinterviewlike thisdialogueinterviewdialoguethe moderatorsifhighlylogicand highlycomputedyou knowthat'syou can be replacedby the algorithmand in the futurethat's a technologyin the futurecan havea verysimilar expressionlike peopleand onlyvery deepemotionand youcan havedifferentfeelingwhen youtalk to himor talk tothis personyou canmakeit'sit's difficultto make friendswith the machinebecause the feelingit takes a long timeit's notthat you can'tmake friendswith the machineif you lookat these moviesand filmsand peoplecan alsofeel in lovewith the robberssometimessoin natureat emotional levelthecomputationis based onlogicsIthe otherviewI fully supportis like thisin factwhen a portionof jobs disappearof coursethe new jobsare createdin the pastwe don'thave so manydata analystswedidn'thave so manypeopleworking asprogrammersand in thefutureon theplatformof AIand wewill call itany kindof industrymay use itany skillsmay use itwhich is availablein anycompaniesin the pastwe are a manufacturerwe have loadsofworkersin the futurewe will havelovedesignersengineersdataanalyststhey willbecomenewprofessionsto meyou needtothink aboutyourselfit's becauseof youyoudidn'tgetpreparedfor the changesso I hopenext yearI willstill be herewill notbe replacedI stillhavetwo to threeminutesand thisis avery very bigquestionsthere'sso manydimensionsof itand wecan onlytouch theservice of itand whatis the mostthingwhichmakesyou worrymostwhatcan'tthinkyou needto getpreparedyou needto getpreparedfor yourselffor yourfor yourfor yourfor yourfor yourfor yourfor yourfor yourfor yourfor yourfor yourfor your20yearsbut youknowthetaster bookwere made10 or 20 yearsagoso therea strongconflict thereI thinkeducationin Chinato abrother sensewe needto havea regulatoryand thelegalframeworkcoinhas twositesyoushouldbefearofI watched a movieit was about ajoanand uavwhich canbecontrolledtokilling peopleand youcan'timaginethatwe needtogetpreparedand ifabusedinfinancialsacredit's alsoscarynotwhatspecificallyyoulearnit willprobably notbe usefulby the time yougraduatebut to learnhow tolearnand tolearna groupof peopleto makethe contextsouniversityis moreaboutconnectingthe networksand Ithinkthis goesback tothe societyfor the reasonsI gave beforesoI guessI worrylet me tell youone more storyabout the jobswe have only30 second leftI bet youyour storyis fantasticwe'll read you articlesbecauseI'llrepeat what you saidI thinkyou're eithergrowing or dyingsowhat can you doto beexcited about changealwaysa constantchangeand we're justseeing itincreasebecause theevolutionsin the digitalworldare notgated byDNAin genomesthe cyclesaremuch fastersoI thinkcuriositycreativityinterpersonalrelationshipslearnhow tolearnbut underneath thatit reallywe havethank youthat's a very greatconclusionyou're eithergrowing or dying非常感謝各位嘉賓今天在這裡給我們分享他們的觀點確實是thank youvery muchfor yoursharingand this isa big waveregardlessof yourpersonalviewand thepeople sayit's alsoa rollercoasterwhat should wedothank youbeing in the bottomthank you


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Carolina Panthers season prediction, how far can rookie quarterback Bryce Young carry this team? Now, let's be honest, the league is not always friendly to rookie quarterbacks, and I'm afraid he's going to start out with a bit of a rough patch after looking at this schedule. I think he's going to lose that opening game to the Falcons, but I think they actually can beat New Orleans on Monday night football before losing four on a road, Seahawks, Vikings, Lions, Dolphins. What a gauntlet that is before the much needed buy week. Then they get the Texans and Colts off the buy, could be a little rookie quarterback action there. I think they win both of those games before dropping L's to both Chicago and the Cowboys, but then I think they beat the Titans and Bucks before losing to the Saints. When they get their revenge against the Falcons in the second match up there, then I think you lose to the Packers and Jaguars before finishing out with a win against Tampa Bay. That gives me a record prediction of seven and ten for the Carolina Panthers this year.