Top 5 Marketing A.I.’s People Won’t Shut Up About


Artificial intelligence is undoubtedly
the future of marketing technology with new machine learning platforms allowing
agencies and brands unparalleled ability to target audiences. I’m Rick Boost and
these are the top five marketing AI systems that people just will not shut
up about. Number 5: Einstein The Einstein AI is named after
history’s most famous scientists and the dog from Back to the Future “Einstein! You little devil!” Einstein is a bit of an odd system to describe unlike the other AIs which are
crafted and pitched as massive super brains, Einstein is relatively simple. It’s
made up of two main comprehension components; a language tool and an image tool. What makes Einstein powerful is its ability to link up to the plethora
of Salesforce platforms. By cross-referencing between them it has
access to a multitude of information. This means that as Salesforce grows its
selection of platforms through acquisitions and partnerships, Einstein
will only grow stronger itself. It’s sort of like the blob, except not quite as
scary. Quite. Number four: Albert. Next up is Albert, which is also named after Einstein! “Einstein! You little devil!” Although to be fair Albert got there first with the name in 2010. Now
Albert is the complete opposite of Einstein because it actually is a
massive super brain. This product is being sold as capable of running an
entire campaign. We’re talking about analytics, insight, strategy, scheduling.
Now a lot of project leads are going to feel very dubious about leaving their
entire campaign in the hands of what amounts to a robot. “YOU WILL SERVICE…US.” Just as many employees are gonna feel a bit nervous themselves about possibly losing their jobs to said robot. “YOU HAVE 20 SECONDS TO COMPLY” The problem is is that Albert has had some very juicy case studies coming in that show (strangely
enough) it works to leave it to the robot. number three Watson Now Watson is exactly what you think of when someone says AI in that Watson is a living
breathing supercomputer that was built by the greatest minds at IBM. It was
built for one purpose and one purpose alone. Some might say the greatest goal
in all humanity…To beat Jeopardy “Watson!” “What is shoe?” “You are right” “Watson!” “Who is Jude?” “Yes” “Watson!” “Who is Michael Phelps?” “Yes” “Watson!” “What is event horizon?” “Who is Grendel?” “What is London?” “What is stick?” “Stick is right.” Yeah. The greatest supercomputer built by mankind was constructed to beat nerds at trivia. But not really. In fact that game show smackdown was just a dry run. It was to
show off if Watson’s natural learning and language abilities were up to snuff,
which they certainly were. From these humble beginnings Watson’s processing
power has been behind the scenes of multiple industries, which now includes
marketing. Watson’s marketing tool set is already able to follow customer journeys
create predictive models to generate unique user experiences on websites and
find visual assets for campaigns, saving valuable time. Watson is even able to
interface with IBM’s weather system, which means that Watson
can now generate strategies based on localized climate in an area.
Sherlock may be smart but Watson is a beast. Number Two: Vidora Vidora has been pitched as being able to understand customers like no other
system before it. Vidora will tag and track individual users on a mass scale
which then means it can follow and then predict their customer behaviour. Now
other systems are capable of doing this, so this sounds kind of simple but Vidora does it on such a level the others don’t, whether a change to the company as a whole or even to a single product will change customer perceptions negatively
or positively. Vidora’s focus is not a weakness. I would see it as a strength. It
is a focused tool it is a scalpel to the butcher knife that other AI’S are. Number One: Marcel *Confused Grunt* Alright, alright, alright for realsies. Okay we all know the drama that happened last year. The place is Cannes and Publicis is announcing in front of all
the other cool kids that they’re done with awards saying, “Like awards are like
2015 lame and you know they’re just gonna move on to an AI that they built.
It’s called Marcel. You know it’s totally hot. And it’s totally real. You know he just
lives in Canada so you haven’t heard of it. The announcement that Publicis was
halting all awards spending to move those resources to building an AI was
nothing short of earth-shattering. People were furious, most notably publicist
staff, who rather like awards since they get to hang them on their agency walls
and they get to put them on their CVs. Whatever Marcel was it would
have to be bloody spectacular. Unfortunately all we’ve been able to
glean about Marcel is that it’s some kind of international matchmaking tool
that project leads can use to connect teams based on skills, location, and
experience. It’s also apparently some kind of management tool in itself and
it’s also some kind of communication platform. So Marcel in theory is
basically if Trello Skype LinkedIn and Tinder all got
together and spawned an amazing abomination.
Publicis only teamed up with Microsoft at the start of this year six months on
from Cannes to properly begin development on this platform. We know nothing really,
apart from a few tweets and a few dodgy statements. Marcel could be anything at
this point so everything I just said in six months from now, one year from now, a century from now (if it take that long to build Marcel) I’ll to be claimed a
complete moron or the Nostradamus of marketing AI Tech. If you would like to
read a far more detailed in-depth explanation of all these AI platforms, as
well as a few we didn’t get around to, definitely click the link below which
has digital experts Eric Thain and Zaheer Nooruddin as well as Or Shani the CEO of
Albert, giving their opinions on all these AI systems and which are their
favorites. Also please leave a comment below about how much I butchered this
video or what you think about AI. Do you think you’re going to accept it into
your lives? Or do you think you’re going to reject it? What is your favorite AI
system? Have you used one? Are there topics you would like us to take on in
future top 5 videos? We want to hear from you! So put it in the comments below and
share the living hell out of this with all your marketing nerds. Do it.

IBM’s Veteran Employment Accelerator


IBM’s Veteran Employment Accelerator Program is part of a very exciting initiative that our CEO announced earlier this year as part of our new collar strategy. So IBM has committed to hire 2000 new Veterans in IBM over the course of the next 4 years. It is sometimes a difficult transition. Especially for the youngsters who knew nothing but high school and war. There was nothing in the civilian world that they knew much about. So getting back to the corporate world and looking for a job, it took help. What we’re here doing is, we train i2 Analyst Notebook software to Veterans so they can go work for the clients. When they come out of this course here, the Veterans, they’ll be able to have a working knowledge of what to do to get through vast amounts of data in whatever career field they decide to go in to, whether it’s law enforcement, cyber security, banking. My name is Chris and I was, prior, in service in the US Army. At IBM, I’m a Law Enforcement Intelligence Analyst. When I first learned of the training opportunity, I was really just trying to pick up a skill set and apply that to future employment. Throughout the week and getting to know the instructors, and as well as other IBMers, the benefit of the training was I got to make those connections. I am Felicia, I was in the US Air Force. I was a computer networking, switching, and cryptosystems technician. I wanted to be in IT. I figured I could get a job almost anywhere since I had done, you know, programmed routers and switches. And I started looking for work and I wasn’t getting picked up by anybody. I couldn’t even get an interview. It was frustrating for me and knowing that I didn’t have the college degree when I first got out and everybody wanted both the college and the experience. So to me it was a double edged sword. It was like, well I have the experience but I don’t have the degree or all the certifications. And so that was one of the things I struggled with and I wish I would have known about the IBM program and Corporate America Supports You before I got out of the military. So my transition for me was very rough and I didn’t think about all these other assistance programs or outreach programs that are there for Veterans. So I was trying to do it on my own. It wasn’t like a lot of other courses where it’s very textbook. It wasn’t very academic, it was very hands on learning, talking about it and really getting you to understand how to manipulate the data. And so we have actually partnered with a number of different customers to hire these Veterans as well. So many have heard a lot about cyber security and fraud. You see it in the news all the time. But there’s a huge opportunity for employment. In the next couple of years, by 2020, you’ll see 1.8 million open jobs in cyber security. We’re up to the point now where every month now we do a program for Veterans and military spouses to get their certification in i2. And then place them into good paying, career type jobs. IBM brings a number of things. It has a tracking technology that allows us to— We can tell you every person that’s been hired, of those 25,000 Veterans. We can tell you first name, last name, military affiliation, company that hired them, what their starting salary was and what they were doing 6 months after they got hired. And I don’t think we could do it without that IBM technology. Early on in the training, the Vets pointed out to us, that this was an opportunity for them to continue to serve, on the domestic front. That they will be expert lead in detecting crime and abuse on the web. One of the things that’s making this initiative so successful is that it is a true partnership. It’s a partnership between IBM, Corporate America Supports You and the Veterans. And so we have really worked closely together to understand the needs of the Veterans and to really open all of the offerings of the IBM company to this partnership.

Watson Assistant for Marketing: This is not a demo.


– All of your data will
produce better results. This is not a demo. If you’re expecting the
usual, this isn’t for you. It can’t be a demo because
this is Watson Assistant for Marketing the AI-powered advisor in a lot of Watson Marketing products. It gets you data and insights faster giving you more time to deliver mind-blowing customer experiences. This is a powerhouse. It helps you pull campaign
reports, evaluate metrics, get support to make smarter
marketing decisions, (cheering) and it makes you look like a superstar. This is fast. Blink-or-you-might-miss-it fast. Did you blink? Because Watson just compared this quarter’s campaign to
last quarter’s, in seconds. This is smart. It’s not a chatbot, it’s a conversation. – What’s up? – Hi, Michael, here’s your dashboard. That happens wherever you are. This is more than a dashboard, it’s a command center that learns with every question you ask. Automatically serving up the info you’re usually looking for. Like comparing your
performance to industry data, identifying relationships within the data, and even predicting performance. – Show me industry benchmarks. – I have found the following benchmarks. Boom, you just accessed benchmarks for 20 industries and nine geos. And it pumps out metrics faster than Bobby the Science Fair King. The click-to-open rate of mailing
Ski Season Opening is 47.6. This is a navigator. – How do I create a mailing template? – Check out these results. That guide you through the
application you’re working in. See, this is not a demo because this is not your
regular marketing tool. It’s the one that changes
the way you do marketing. (drumming)
This is Watson Assistant for Marketing.

How Blockchain Is Disrupting Global Business | Sibos 2019


Do you think that cryptocurrencies will potentially
see their end? No, I don’t. Some of these technologies, by their very
nature, are quite provocative to the status quo. Everywhere we look, probably around every
case, there’s at least 30 percent of process inefficiency. Now, more than 90 percent of the top 50 global
banks are doing at least one experiment in blockchain in trade finance. Welcome to SIBOS 2019. Since its inception 41 years ago, this is
become the premier financial services event of the world, featuring more than 11,000 delicates,
600 speakers and 300 exhibitors from all over the globe. Hosted by financial payments powerhouse Swift,
this conference is pretty much a bankers paradise. Over the course of two days, we managed to
sit down with eleven experts from some of the most prominent financial institutions
consulting firms, research groups, fintech developers and open source ledgers in the
game today, including IBM, Accenture, McKinsey and Company, ANZ, Hyperledger, Fnality, Synechron,
and Everest Research Group. And yet, despite being inside the belly of
the beast, we found that blockchain was still on the tip of everyone’s tongue, and not for
the reasons you might think. We are here at Sibos.I’m with Jason Kelly from IBM. Could you please just tell me more about what
you’re doing here why you’re here and what you’re doing at IBM? So first, it’s Sibos, one of the premier banking
conferences in the world. IBM has a history, has a deep history in banking. However, I’m here as the blockchain general
manager for IBM and general manager of blockchain means that we as a blockchain company, that
is also a cloud company. It is also a services company, that is also
a convener of network company. We’re here to talk to clients to get them
understanding that blockchain isn’t just a big, bright, shiny thing, that says B-word,
but instead it’s it’s a true capability that’s helping to transform this industry along with
others. Why is it a true capability? People tend to say blockchain and they think
crypto. Oh, yes, crypto think that that goes way back. This is not crypto. Now blockchain is under that is the capability
under that. And that’s what we focus on. What’s the true outcome of blockchain so that
you get these things that you’ve been chasing in your enterprise that are focused on one
thing, one thing only at the center, and that’s data and that’s what’s been elusive. So we blockchain thing, what is it? It’s just driving clarity around two things
and that’s having trusted permission, access to data. Once you get to that data, you know what’s
right. So think about you as a supplier. Perhaps if you’re a producer or supplier,
there’s a sort of supply chain there. Right now you have many steps along a supply
chain with a consumer being at the end of that. I think if you have trusted shared access,
you don’t have to make phone calls, look at emails and check and see where something is
in the supply chain. You could all have access, trusted access
by just looking at it. And then when you saw that data and you had
access, you’d know it was right. That’s what blockchain is delivering as a
capability on the forefront. Then we think about all of the extra. Guess what else depends on data? IOT. So the Internet of Things, if you’re trying
to make sure that that device is that device and it’s saying how something’s going to it’s
using data as well. AI also is using data automation using data. All of these things are now being empowered
by this new catalyst of capability, which is blockchain. Hold on a minute. Let’s take a step back. This doesn’t sound like the blockchain we’re
used to talking about. What exactly are we dealing with here? Let’s take one of the use cases for blockchain,
for companies, for me, permission networks. And the idea for permissioned networks is that there are a number of companies, let’s say that want to create some network together,
let’s say supply chain, logistics, trade, finance, all banks. They just want to do the cross-border payments
in the you know, more efficient way. And they create the blockchain network where
they will put transactions and they will be and they will transparent to everyone and
every transaction will go in based on the agreement between the parties. So let us decide with at least 2 out of 10
participants to agree on transactions in order to be to further that transaction to be added
to the ledger. When people hear blockchain, they tend to
think of the two most common consensus algorithms associated with cryptocurrencies, proof of
work and proof of stake. These are mostly used for public or permissionless
networks where transaction validators don’t need permission from anyone to verify transactions,
just the proper tools. Here at Sibos we are going to take a closer
look at the other side of blockchain to see how modern companies are integrating permission
or private blockchain networks into their infrastructures. So first, let’s gain an understanding about
where and how this technology can actually be applied. I’m Ronak Doshi, I’m vice president of Everest
Group, so we are a boutique research and advisory firm focused on the global services industry. One of the key research areas for me is blockchains. So we’re seeing four types of key use cases. The first one is what I call ‘alpha in the
room’ now in ‘alpha in the room’ what happens is you have a single entity which has disproportionate
power and can get other entities in the room together. It could be a regulator, it could be the form
that has the maximum market share in that area, or it’s a vendor relationship where
the buyer has more power, like, say, a Wal-Mart, like a bigger giant, can control all its windows
and can dictate the terms. That’s where the alpha in the room construct
this. This is where we have seen the maximum networks
forming because you have a single entity who’s trying to drive this agenda of blockchain
adoption and trying to create better processes and kind of driving efficiencies. The second area is the ‘bottom line collaborators’. The ‘bottom line collaborators’ are trying
to reduce the cost of doing the business or the process by coming together. This is where you would see areas such as
trade finance, area such as digital identity or provenance or seeing the entire supply
chain financing. All of these are on international payments
and settlements. These are key areas where you’re seeing this
happening. The third category is what we would call as
think of it as this ‘disintermediation avoiders’. ‘The disintermediation avoiders’ are central
agencies who are doing settlements, are the ones who are doing cloud protections processing,
and they are the ones who now are in track of getting disintermediated because of technology
but the beauty of this is a lot of these guys. You know, if you look at the Australian Stock
Exchange and a lot of these market intermediaries have already started their blockchain journey. So they are now thinking of blockchain as
an enabler for their business so that they can protect their business. But at the same time, creating new value added
streams of wealth. So create new revenue streams on your value
for their customers so that they can retain that business. And the fourth category is what I would call
as a ‘disruption category’, which is more like, you know, the examples of cryptocurrencies
and bitcoin and all of these guys were coming together and creating newer ways of working
like like the peer to peer and marketplaces and that. So in all of these situations, you need ecosystems
or networks to get created. The purpose is different. It could be cost. In some cases it could be disintermediation
avoidance in some cases. In some cases it’s about creating your values,
a business model. So we are seeing signs of all of this happening. Let’s put that in some context. What would blockchain technology look like
if it was applied to a real life system? We’ve been fascinated by it ever since the
emergence of Bitcoin and we took a deep dive into that. And that’s where we kind of started realizing
the data reconciliation properties of blockchain. Subsequent to that, we got challenged domestically
in Australia by some large property companies who were asking us to solve a big problem. Banks have been issuing paper to their clients
for a long time, for decades. These companies, large landlords came to us
and said: you got to stop that. So we decided that we would look to digitize
and standardize the product, but we also would try to select the appropriate technology to
support a network of users who were competitors to a number of actors who were either applicants
or beneficiaries and without creating a large central capability or function. Now, unsurprisingly, blockchain presented
itself as a really interesting opportunity. So we leverage the IBM blockchain platform,
which is a hyperledger based code, to deliver a platform where we could digitize standardised
guarantees, put it on that platform and give our large beneficiary customers, landlords
in this case, a substantial uplift in their experience guarantees paper to digital, standardization,
security and in quite a significant amount of workflow. It does enable all of those great benefits,
that reduction in friction, dramatic change in lifecycle time and also the workflow that
we generated through the UI that sat on the blockchain. So this was a key example of how blockchain
was integrated by a large bank to digitize and improve the efficiency of an existing
system. So let’s think about this on a larger scale. If this is just one example of a process being
streamlined through blockchain, what kind of impact would this have across an entire
industry? Is the impact quantifiable? Everywhere we look, probably around every
use case, at least 30% of process inefficiency. It’s just about everybody in the ecosystem. The idea is to get the frictionless processing,
right. Every piece of friction in that process today
causes delays, right, so it causes either delays in people, time or money. Some goods sitting in a board that can’t be
released until they can confirm that the funds were exchanged hands. Like that’s time in your inventory, right. You’re paying for inventory that’s sitting
idle and you’re working capital, a capability. That gives you the ability to faster get to
that working capital so you can spend it on the right things. You think of the ability to look across that
data and have different triggers you can do now depending on who you are in the ecosystem. You’ve got goods that you now know the location
of and your insurer. You might want to offer insurance as there’s
a weather event coming across the ocean. There’s all kinds of new services that people
can make. So there’s cost savings, which are exciting,
right. 30% cost savings or more, it’s fantastic. I can do new things, right. I can offer new products and services and
new people are going to start to enter the market as they have access to that information. This all sounds great, but is it as revolutionary
as it sounds? Is blockchain technology truly applicable
to these kinds of situations? Blockchain technology favors the crypto use
case far more. It is built for that reason. And, you know, the banking sector is trying
to sort of shimmy it to make it work for their needs and their operational challenges. And that’s the way not proven yet. Whether that’s going to work. But I think certainly the technology is a
unique way to achieve that censorship resistant currency story. What is the case in which banks can actually
recognize the potential of this technology and use it in a way that isn’t just shoehorned
into the existing infrastructure? I think the discussion that utility settlement
coin and finality are pushing is a really interesting story. That’s a that is a potentially a unique way
that blockchain can grid can unlock the fact that so the financial system requires banks
to lock up capital in a very crude way. Basically, every bank has a bank account with
each other and stores cash there. And so the finality story of fatality stories
is a promise to unlock that all that money that’s stuck in bank accounts for interbank
transfers. And that’ll be at an enormous cost saving. Can you break that down a little bit? So you’re using something that’s called the
utility service utility settlement coin. So what exactly is that? So the core idea is that we can represent
a cash asset digitally on a shared ledger, a blockchain, where the actual cash represented
by the digital claim is held on deposit in a central bank with some of the characteristics
of central bank money, things like free from counterparty risk, free from credit risk settlement
finality, which is actually where the name finality comes from for finality international. The idea is, however, that even though the
digital claim would have some of the characteristics of central bank money, it’s not actually central
bank money itself. It’s a very unique form of commercial bank
money. It’s really designed for wholesale settlement
between financial institutions and it’s designed to be multi-currency in that it wouldn’t just
be one claim sitting on one ledger, but will ultimately be claims against different currencies
pounds, euros, dollars, Canadian dollars, Japanese yen, etc. on different ledgers. So what is the main impact of this coin on
the two companies using it themselves? So longer term, it’s really about automation
and efficiency, which seems to be the story in financial markets for the last 50 years. And the idea is that at some point in the
near future we will have a multi-currency clearing and settling system for wholesale
purposes that runs 24/7. 365 could substantially reduce operational
risk, reduce capital consumption because of the change in the amount of that’s a risk
rate of the assets that need to be allocated against the cash that banks currently use
to fund their payments activities across the world. It’s a big vision. It’s something that is going to take some
time to realize its scale. But we’re on the path. So we’ve been talking to a lot of different
people over these past couple of days. And one thing that is on their mind is blockchain. We’ve talked of banks, consulting firms and
fintech companies, and all of them are doing research or looking to pilot or implement
blockchain technology. A year ago, everyone’s talking about cryptocurrencies,
but now everyone seems to be talking about blockchain technology. And what about other projects outside of the
banking sector? How is blockchain revolutionizing the way
other systems work? My name is Marta Geater-Piekarska. I’m director of ecosystem for Hyper Ledger. So Hyper Ledger is an open source project
within the Linux Foundation. So we are non-profit, hosted by one of the
bigger non-profit technologies in the world. We started three years ago as an answer to
what can enterprises and the business community do with blockchain. So not really the bitcoin side of things open
public permissionless blockchains more the permission public or private blockchain technologies. We are still a bit in a phase of kind of throwing
spaghetti to the wall and seeing what sticks. So this is changing. We are getting more and more focused. All industries are getting more focused. Understanding that blockchain won’t solve
it all and it’s all about the bigger picture and the bigger solution. So I think that in trade, finance and supply
chain, we will see more blockchain stuff for kind of tracking the provenance, the blockchain
being integrated into full supply chain from, you know, from the farmer all the way to the
consumer connecting consumer and farmers so that I know that it’s Joe the farmer that
grows my coffee and not some random person that I’ve you know, I have no connection to. So it’s more kind of a personalized experience
for the users. In terms of other products. I think identity will becoming more and more
interesting, actually. You know, Accenture has a very interesting
demo of something that is called Known Traveler Identity, which breeds a seamless identity
kind of based system for travelling. My name is David Treat. I lead Accenture’s blockchain business globally
along with a guy named Simon Whitehouse. I’ve been in the space leading this business
now for the past four years, building it up from where it existed in Accenture Labs as
a pure R&D project for the few years before that. But I’ve been in the blockchain space for
close to six and a half years now. So what’s the most interesting thing that
Accenture is working on at the moment? We have three focal areas really where we’re
putting the majority of our investment. It’s financial services, infrastructure, digital
identity and supply chain, both physical and digital. And really, oftentimes all three of those
come together. But being here in Sibos, you know, financial
services, infrastructure and and digital identity are, of course, key on the digital digital
identity. The importance of that is really a core foundational
capability that involves multiple innovative technology spaces, not just blockchain. It’s blockchain plus biometrics. It’s all about the linkage between the physical
and the digital world. The winning digital business today wins by
accumulating as much data as they possibly can and then feeding it into their A.I. machine
learning algorithms and generating marketing insights and the rest who we know control. The first foundational thing that this new
combination of technologies creates is that for the first time ever, I can encode three
core things. I can encode the intent, the rights and the
obligations of the data as I share it with you. So the intent I want you to have this piece
of data. I’m going to cryptographically sign it. I want you to have it. You then store it and you’re storing it with
a set of rights that I’ve given you as to what you can do with it. And this is all codified in the data and then
a set of obligations that you owe back to me. So this is the control aspect of suddenly
through the cryptography, through the through this notion of user controlled digital identity. I can take those specific pieces of data elements
and I can intentionally share them with you for specific purposes. Importantly, we’ve also created as a community
the ability for me to revoke it. I can say I only want you to have it for 10
minutes or I want you to have it until I tell you that I no longer want you to have it. And I can pull it back. We’ve never had that ability before. So we have this notion of me having a personal
data store that’s my digital identity wallet that I accumulate, my passport information,
my driver’s license, my medical information, work credentials, etcetera. And I now have this data of attested information
from service providers and authorities. The interesting thing that, you know, there
is, of course, you have to directly link the digital world to the physical world. And so biometrics is the way to do that, right. Whether it’s face or iris or fingerprint or
DNA, at some point, you know, it’s the whole notion of linking that physical and digital
world. And so the pattern is I have a personal data
store. I’m able to intentionally share that data
with others. I’m able to have control over it. And I’m encoded as some of into some of those
attestations are biometrics attestations so that then someone can verify that yes, that
digital information belongs to that human. Reclaiming my identity. Right. Exactly. So what are the actual use cases for this
technology? So one of the ones we’re most proud of and
is and is closest to a production implementation is work that we’re doing with the countries
of country of Canada, the Kingdom of Netherlands, Schiphol Airport, Toronto, Montreal Airports,
KLM Airlines and Air Canada as a starting group of an ecosystem that’s looking at the
massive problem of the travel industry’s growth. We’re set to, I think, you know, close to
double the amount of travelers in the next 10 years. The current infrastructure just won’t be able
to support it. And so the whole notion is with this self-managed
user, you know, user managed digital identity, I can take that attested information and share
my details ahead of time intentionally with those that I’m going to interact with on my
trip. They can pre certify, pre-clear me and then
it doesn’t have to be a surprise when I show up at immigration and pin my passport over. They shouldn’t be surprised to see me. Instead, I book my ticket hour, you know,
hours at a minimum. But but days, weeks, months ago they should
know I was coming. They have me in context that we can use facial
recognition technology. I just walk right through. So it’s kind of a base case. But build on build from there. Right. I share that information on my hotel, the
car services, the restaurants I want to attend to go to. I’m sharing the pieces that I want to choose
to share with all the p players that I want to have provide me hyper personalized services
in a very controlled, private, secure, revocable manner. Suddenly, I can create an end and traveler
experience around my own management of my own digital identity information. So I won’t have to wait for two hours at the
airport anymore. I just walk straight through. Oh yeah. Or if you’re attending a giant conference
and there’s a long queue at the long queue to get in, maybe you just walk right through
because that’s really you pre-registered and through facial recognition. You just you know, you’re in. These sound like some great ideas. But why haven’t we seen anything come to fruition
yet? What are the hangups? What is holding blockchain back from being
fully integrated? Is there anything? So, first of all, I would say that is still
an element of the cost of the technology, different elements, acquiring the right capabilities. Developing the technology, the computational
power that is needed for some let me say of the chains. This is something that I think from a technology
perspective needs to be matured. The second element, which I believe is a bit
of a roadblock, is the lack of standardization. Right. So, I mean, the banking sector has always
pushed for a interoperable standards at global level or at a domestic level. In the absence of a stand that it’s different
for the four that banks are to communicate with each other and given the distributed
nationality of blockchain. And the fact that multiple actors needs to
interact on the same network this could be actually a huge challenge. The third element, which I would mention is
the fact that some of the blockchain technology that we’ve seen, for instance, for coins where
intrinsically not fit for purpose for financial services application at scale. So for instance, now we see a clear trend
of financial players it’s investing in to private permissioned chains. While the very first one where public and
permissionless. Right. So I mean, this is part of the evolution of
the technology that is undergoing. That has been also an investment from the
financial community in this respect. And probably this might be overcome, let’s
say, in the next few months. So with all this talk about mainstream blockchain
adoption, where does that leave crypto? So what is your overall opinion on cryptocurrencies
and specifically cryptocurrencies like bitcoin, ones that are meant to challenge the centralized
infrastructure that we see today? I think they’ve been a tremendous catalyst
for change within banking. I think they’re fascinating because of the
way they’ve just emerged from from nowhere. And there’s way too many altcoins out there
right now to even understand what everyone’s for. There are some emerging technologies that
are that are much, much more centered around enterprise use cases which are starting to
show some promise. But I think cryptocurrencies they will always have a place at the periphery
of our economy. So you don’t see a place for something like
Bitcoin in the mainstream in the future? No. Because I think it doesn’t have the properties
that are attractive for high speed transmission of value. It’s too slow, right? The T.P.S. is way too low. You know, 7 T.P.S. or 10 T.P.S. versus the
MasterCard Visa Network, which is twenty thirty thousand T.P.S.. It’s not fit for purpose for mainstream adoption
because of that. I think its governance is difficult for us
to to support or lack thereof I should say and the regulators, you know, you have to
ask them. But I get the impression that the door is
open for a set of digital assets to emerge in our economy. But for those, I guess, you know, digital
coin 1.0, I think they’ll have their place in history, but they have to evolve. They don’t evolve and become more customer
friendly, faster, more secure and more transparent then there’ll be other coins that emerge private
or or perhaps central bank-issued that will overtake them because to just be others had
better features and capability. So do you think that cryptocurrencies will
potentially see their end? No, I don’t. So bankers don’t love volatility. Their customers don’t like volatility. But we didn’t lose sight of the fact that
those currencies, be they as volatile as they were at the time, and they are much more stable
now they nailed the clearing and settlement problem the bankers have. So my intuition said that if we can get a
stable coin and you see much more talk about stable coins now, including Libra, for example,
that can deliver the properties of a cryptocurrency which synchronizes clearing and settlement,
you can transform the payments process beyond any reasonable expectation that correspondant
banking might traditionally attempt to do. So you’ve got to take the right features and
capability out of the wild cryptocurrencies, if you like. Put them into a more regulated framing. But at the same time, not losing those really
important features that say instant transmission, synchronized clearing and settlement and ideally
a really strong digital wallet experience for a customer. Through the advent of cryptocurrencies the world seems to be finally taking blockchain
technology seriously. But as we look to the future will blockchain
remain in the forefront of innovation in the financial world? How do you see the relevance of blockchain
technology in the future? Do you think it’s this big like a technological
revolution that people are saying, like comparing it to the Internet and those things like that? I think it’s an interesting thing. The true success of it will be when it becomes
invisible, actually. So I think we won’t be talking about blockchain
or technologies when it’s already part of sort of the day to day life that we have. So I think that’s that’s ultimate test. I sort of see that at the end, you know, we
don’t really talk about some of the technologies anymore that have transformed our lives as
much. So I think the true success of it will be
when we don’t talk about it anymore and it’s embedded everywhere and we just accept it
as it is. This has been Sibos twenty nineteen where
I’ve talked to some of the biggest names in finance. And if there’s one thing I’ve learned from
all of this, it’s that blockchain technology is here to stay. I’m Jackson with Cointelegraph and I’ll see
you down the road. Adios, amigos. Cointelegraph. Like, Subscribe and Hodl.

Don’t fear intelligent machines. Work with them | Garry Kasparov


This story begins in 1985, when at age 22, I became the World Chess Champion after beating Anatoly Karpov. Earlier that year, I played what is called
simultaneous exhibition against 32 of the world’s
best chess-playing machines in Hamburg, Germany. I won all the games, and then it was not considered
much of a surprise that I could beat 32 computers
at the same time. To me, that was the golden age. (Laughter) Machines were weak, and my hair was strong. (Laughter) Just 12 years later, I was fighting for my life
against just one computer in a match called by the cover of “Newsweek” “The Brain’s Last Stand.” No pressure. (Laughter) From mythology to science fiction, human versus machine has been often portrayed
as a matter of life and death. John Henry, called the steel-driving man in the 19th century
African American folk legend, was pitted in a race against a steam-powered hammer bashing a tunnel through mountain rock. John Henry’s legend
is a part of a long historical narrative pitting humanity versus technology. And this competitive rhetoric
is standard now. We are in a race against the machines, in a fight or even in a war. Jobs are being killed off. People are being replaced
as if they had vanished from the Earth. It’s enough to think that the movies
like “The Terminator” or “The Matrix” are nonfiction. There are very few instances of an arena where the human body and mind
can compete on equal terms with a computer or a robot. Actually, I wish there were a few more. Instead, it was my blessing and my curse to literally become the proverbial man in the man versus machine competition that everybody is still talking about. In the most famous human-machine
competition since John Henry, I played two matches against the IBM supercomputer, Deep Blue. Nobody remembers
that I won the first match — (Laughter) (Applause) In Philadelphia, before losing the rematch
the following year in New York. But I guess that’s fair. There is no day in history,
special calendar entry for all the people
who failed to climb Mt. Everest before Sir Edmund Hillary
and Tenzing Norgay made it to the top. And in 1997, I was still
the world champion when chess computers finally came of age. I was Mt. Everest, and Deep Blue reached the summit. I should say of course,
not that Deep Blue did it, but its human creators — Anantharaman, Campbell, Hoane, Hsu. Hats off to them. As always, machine’s triumph
was a human triumph, something we tend to forget when humans
are surpassed by our own creations. Deep Blue was victorious, but was it intelligent? No, no it wasn’t, at least not in the way Alan Turing
and other founders of computer science had hoped. It turned out that chess
could be crunched by brute force, once hardware got fast enough and algorithms got smart enough. Although by the definition of the output, grandmaster-level chess, Deep Blue was intelligent. But even at the incredible speed, 200 million positions per second, Deep Blue’s method provided little of the dreamed-of insight
into the mysteries of human intelligence. Soon, machines will be taxi drivers and doctors and professors, but will they be “intelligent?” I would rather leave these definitions to the philosophers and to the dictionary. What really matters is how we humans feel about living and working
with these machines. When I first met Deep Blue
in 1996 in February, I had been the world champion
for more than 10 years, and I had played 182
world championship games and hundreds of games against
other top players in other competitions. I knew what to expect from my opponents and what to expect from myself. I was used to measure their moves and to gauge their emotional state by watching their body language
and looking into their eyes. And then I sat across
the chessboard from Deep Blue. I immediately sensed something new, something unsettling. You might experience a similar feeling the first time you ride
in a driverless car or the first time your new computer
manager issues an order at work. But when I sat at that first game, I couldn’t be sure what is this thing capable of. Technology can advance in leaps,
and IBM had invested heavily. I lost that game. And I couldn’t help wondering, might it be invincible? Was my beloved game of chess over? These were human doubts, human fears, and the only thing I knew for sure was that my opponent Deep Blue
had no such worries at all. (Laughter) I fought back after this devastating blow to win the first match, but the writing was on the wall. I eventually lost to the machine but I didn’t suffer the fate of John Henry who won but died
with his hammer in his hand. [John Henry Died with a Hammer in His Hand
Palmer C. Hayden] [The Museum of African
American Art, Los Angeles] It turned out that the world of chess still wanted to have
a human chess champion. And even today, when a free chess app
on the latest mobile phone is stronger than Deep Blue, people are still playing chess, even more than ever before. Doomsayers predicted
that nobody would touch the game that could be conquered by the machine, and they were wrong, proven wrong, but doomsaying has always been
a popular pastime when it comes to technology. What I learned from my own experience is that we must face our fears if we want to get the most
out of our technology, and we must conquer those fears if we want to get the best
out of our humanity. While licking my wounds, I got a lot of inspiration from my battles against Deep Blue. As the old Russian saying goes,
if you can’t beat them, join them. Then I thought, what if I could play with a computer — together with a computer at my side,
combining our strengths, human intuition
plus machine’s calculation, human strategy, machine tactics, human experience, machine’s memory. Could it be the perfect game ever played? My idea came to life in 1998 under the name of Advanced Chess when I played this human-plus-machine
competition against another elite player. But in this first experiment, we both failed to combine
human and machine skills effectively. Advanced Chess found
its home on the internet, and in 2005, a so-called
freestyle chess tournament produced a revelation. A team of grandmasters
and top machines participated, but the winners were not grandmasters, not a supercomputer. The winners were a pair
of amateur American chess players operating three ordinary PCs
at the same time. Their skill of coaching their machines effectively counteracted
the superior chess knowledge of their grandmaster opponents and much greater
computational power of others. And I reached this formulation. A weak human player plus a machine plus a better process is superior to a very powerful machine alone, but more remarkably,
is superior to a strong human player plus machine and an inferior process. This convinced me that we would need better interfaces
to help us coach our machines towards more useful intelligence. Human plus machine isn’t the future, it’s the present. Everybody that’s used online translation to get the gist of a news article
from a foreign newspaper, knowing its far from perfect. Then we use our human experience to make sense out of that, and then the machine
learns from our corrections. This model is spreading and investing
in medical diagnosis, security analysis. The machine crunches data, calculates probabilities, gets 80 percent of the way, 90 percent, making it easier for analysis and decision-making of the human party. But you are not going to send your kids to school in a self-driving car
with 90 percent accuracy, even with 99 percent. So we need a leap forward to add a few more crucial decimal places. Twenty years after
my match with Deep Blue, second match, this sensational
“The Brain’s Last Stand” headline has become commonplace as intelligent machines move in every sector, seemingly every day. But unlike in the past, when machines replaced farm animals, manual labor, now they are coming
after people with college degrees and political influence. And as someone
who fought machines and lost, I am here to tell you
this is excellent, excellent news. Eventually, every profession will have to feel these pressures or else it will mean humanity
has ceased to make progress. We don’t get to choose when and where
technological progress stops. We cannot slow down. In fact, we have to speed up. Our technology excels at removing difficulties and uncertainties
from our lives, and so we must seek out ever more difficult, ever more uncertain challenges. Machines have calculations. We have understanding. Machines have instructions. We have purpose. Machines have objectivity. We have passion. We should not worry
about what our machines can do today. Instead, we should worry
about what they still cannot do today, because we will need the help
of the new, intelligent machines to turn our grandest dreams into reality. And if we fail, if we fail, it’s not because our machines
are too intelligent, or not intelligent enough. If we fail, it’s because
we grew complacent and limited our ambitions. Our humanity is not defined by any skill, like swinging a hammer
or even playing chess. There’s one thing only a human can do. That’s dream. So let us dream big. Thank you. (Applause)

How do hard drives work? – Kanawat Senanan


Imagine an airplane flying
one millimeter above the ground and circling the Earth
once every 25 seconds while counting every blade of grass. Shrink all that down so that it fits
in the palm of your hand, and you’d have something equivalent
to a modern hard drive, an object that can likely hold
more information than your local library. So how does it store so much information
in such a small space? At the heart of every hard drive
is a stack of high-speed spinning discs with a recording head
flying over each surface. Each disc is coated with a film
of microscopic magnetised metal grains, and your data doesn’t live there
in a form you can recognize. Instead, it is recorded
as a magnetic pattern formed by groups of those tiny grains. In each group, also known as a bit, all of the grains have
their magnetization’s aligned in one of two possible states, which correspond to zeroes and ones. Data is written onto the disc by converting strings of bits
into electrical current fed through an electromagnet. This magnet generates a field
strong enough to change the direction of the metal grain’s magnetization. Once this information is written
onto the disc, the drive uses a magnetic reader
to turn it back into a useful form, much like a phonograph needle
translates a record’s grooves into music. But how can you get so much information
out of just zeroes and ones? Well, by putting lots of them together. For example, a letter is represented
in one byte, or eight bits, and your average photo
takes up several megabytes, each of which is 8 million bits. Because each bit must be written onto
a physical area of the disc, we’re always seeking to increase
the disc’s areal density, or how many bits can be squeezed
into one square inch. The areal density of a modern hard drive
is about 600 gigabits per square inch, 300 million times greater than that
of IBM’s first hard drive from 1957. This amazing advance in storage capacity wasn’t just a matter
of making everything smaller, but involved multiple innovations. A technique called the thin film
lithography process allowed engineers
to shrink the reader and writer. And despite its size,
the reader became more sensitive by taking advantage of new discoveries in
magnetic and quantum properties of matter. Bits could also be packed closer together
thanks to mathematical algorithms that filter out noise
from magnetic interference, and find the most likely bit sequences
from each chunk of read-back signal. And thermal expansion control of the head, enabled by placing a heater
under the magnetic writer, allowed it to fly less than
five nanometers above the disc’s surface, about the width of two strands of DNA. For the past several decades, the exponential growth in computer
storage capacity and processing power has followed a pattern
known as Moore’s Law, which, in 1975, predicted that information
density would double every two years. But at around 100 gigabits
per square inch, shrinking the magnetic grains further
or cramming them closer together posed a new risk
called the superparamagnetic effect. When a magnetic grain volume is too small, its magnetization is easily disturbed
by heat energy and can cause bits
to switch unintentionally, leading to data loss. Scientists resolved this limitation
in a remarkably simple way: by changing the direction of recording
from longitudinal to perpendicular, allowing areal density to approach
one terabit per square inch. Recently, the potential limit has been
increased yet again through heat assisted magnetic recording. This uses an even more thermally
stable recording medium, whose magnetic resistance
is momentarily reduced by heating up a particular spot
with a laser and allowing data to be written. And while those drives are currently
in the prototype stage, scientists already have the next potential
trick up their sleeves: bit-patterned media, where bit locations are arranged
in separate, nano-sized structures, potentially allowing for areal densities
of twenty terabits per square inch or more. So it’s thanks to the combined efforts
of generations of engineers, material scientists, and quantum physicists that this tool of incredible power
and precision can spin in the palm of your hand.

Data Science Methodology 101 – Business Understanding Concepts and Case Study


Welcome to Data Science Methodology 101 From
Problem to Approach Business Understanding! Has this ever happened to you? You’ve been called into a meeting by your
boss, who makes you aware of an important task one with a very tight deadline that
absolutely has to be met. You both go back and forth to ensure that
all aspects of the task have been considered and the meeting ends with both of you confident
that things are on track. Later that afternoon, however, after you’ve
spent some time examining the various issues at play, you realize that you need to ask
several additional questions in order to truly accomplish the task. Unfortunately, the boss won’t be available
again until tomorrow morning. Now, with the tight deadline still ringing
in your ears, you start feeling a sense of uneasiness. So, what do you do? Do you risk moving forward or do you stop
and seek clarification. Data science methodology begins with spending
the time to seek clarification, to attain what can be referred to as a business understanding. Having this understanding is placed at the
beginning of the methodology because getting clarity around the problem to be solved, allows
you to determine which data will be used to answer the core question. Rollins suggests that having a clearly defined
question is vital because it ultimately directs the analytic approach that will be needed
to address the question. All too often, much effort is put into answering
what people THINK is the question, and while the methods used to address that question
might be sound, they don’t help to solve the actual problem. Establishing a clearly defined question starts
with understanding the GOAL of the person who is asking the question. For example, if a business owner asks: “How
can we reduce the costs of performing an activity?” We need to understand, is the goal to improve
the efficiency of the activity? Or is it to increase the businesses profitability? Once the goal is clarified, the next piece
of the puzzle is to figure out the objectives that are in support of the goal. By breaking down the objectives, structured
discussions can take place where priorities can be identified in a way that can lead to
organizing and planning on how to tackle the problem. Depending on the problem, different stakeholders
will need to be engaged in the discussion to help determine requirements and clarify
questions. So now, let’s look at the case study related
to applying “Business Understanding” In the Case Study, the question being asked
is: What is the best way to allocate the limited healthcare budget to maximize its use in providing
quality care? This question is one that became a hot topic
for an American healthcare insurance provider. As public funding for readmissions was decreasing,
this insurance company was at risk of having to make up for the cost difference,which could
potentially increase rates for its customers. Knowing that raising insurance rates was not
going to be a popular move, the insurance company sat down with the health care authorities
in its region and brought in IBM data scientists to see how data science could be applied to
the question at hand. Before even starting to collect data, the
goals and objectives needed to be defined. After spending time to determine the goals
and objectives, the team prioritized “patient readmissions” as an effective area for review. With the goals and objectives in mind, it
was found that approximately 30% of individuals who finish rehab treatment would be readmitted
to a rehab center within one year; and that 50% would be readmitted within five years. After reviewing some records, it was discovered
that the patients with congestive heart failure were at the top of the readmission list. It was further determined that a decision-tree
model could be applied to review this scenario, to determine why this was occurring. To gain the business understanding that would
guide the analytics team in formulating and performing their first project, the IBM Data
scientists, proposed and delivered an on-site workshop to kick things off. The key business sponsors involvement throughout
the project was critical, in that the sponsor: 1. Set overall direction
2. Remained engaged and provided guidance
3. Ensured necessary support, where needed Finally, four business requirements were identified
for whatever model would be built. Namely: 1. Predicting readmission outcomes for those
patients with Congestive Heart Failure 2. Predicting readmission risk
3. Understanding the combination of events that
led to the predicted outcome 4. Applying an easy-to-understand process to
new patients, regarding their readmission risk. This ends the Business Understanding section
of this course. Thanks for watching!

Sole Cooperativa Working with IBM to Leverage Internet of Things in Senior Housing


Question: Mrs. Massi, can you please introduce Cooperativa Sole? Cooperativa Sole is a dynamic, strong and go-ahead enterprise, able to propose innovative and effective caring models. Able to offer dignity, well-being and safety to our cared ones. We offer different types of services. from communities for individuals with dual diagnosis, to communities with psychiatric patients, nursing homes, and summer residences for older adults. We provide services for occupational medicine and, for the last couple years, we have been supporting co-housing projects. Question: What it is the purpose of your collaboration with IBM? To test new technologies in order to build innovative caring systems. Question: what are the main issues you want to address through technology? We see two main areas: the first is safety through monitoring of the individual biomedical parameters. The second is a higher quality of life. Question: what are the outcomes you expect to achieve? We want to show that it is possible to envision a new way of caring for the aging and fragile population. Delivering different answers to the needs of people. Question: What are, in your experience, the typical technological gaps of the aging facing technology? For sure most of the older adults do not know the possibilities offered by emerging technologies. Most of the times they are not supported in using it, and a proper communication is missing. Too often technology is perceived as expensive so it is not considered as a possibility.