>> Emerging technologies.
The learning objectives for this lesson are
to describe blockchain concepts,
to explain artificial intelligence and augmented reality,
and to discover big data,
deep learning, and quantum computing.
Let's get started. Blockchain. You're probably
are familiar with this at
some level because you've heard of Bitcoin.
Bitcoin is the most well-known example
of the blockchain in use.
But the blockchain can be used for
many different types of problem-solving.
The blockchain at its basic level is an expanding list of
transactional records that are secured by cryptography.
Each record is known as a block,
and then they're all connected together in the chain.
Each block is hashed and
the hash value of the previous block is included with it.
This way, each block
validates the next block in the chain.
One thing to keep in mind is that the blockchain is
a public ledger that is
distributed across peer-to-peer networks.
Back to Bitcoin, a lot of people
think that Bitcoin is anonymous.
Now we might not know who owns a specific wallet address,
but every transaction that wallet has ever done in or
out is available for everyone to see on the blockchain.
This is something that's not commonly understood.
But since it is a public ledger,
anyone can view the records.
Those wallets again, are not anonymous.
We don't know who owns it necessarily,
but we do know what that wallet has been doing.
Secure multi-party computation.
This is distributing
computations across multiple systems.
But the key is that no individual system
is able to read the data of
the other parties in this grouping.
This allows us to solve
complex problems without compromising
the privacy of the data.
Some examples of when we would use this maybe would be
DNA research or research into patient data.
Distributed consensus.
A distributed or decentralized system where
all the systems come to
an agreement for a specific computation.
We're doing this to maintain
the overall integrity of a distributed system.
Because we know that some of the systems that are
included in this might be malicious,
the distributed consensus takes
a vote from all of the systems about the data.
The value with the most votes
is the one that gets accepted.
Artificial intelligence.
This is the science of creating
computing systems that can
simulate or demonstrate intelligence
that's similar to the level of a human.
Machine learning uses algorithms to parse data,
then develop strategies for using that data.
Machine learning can modify the algorithms and
make gradual improvements in
its ability to make decisions.
Adversarial artificial intelligence.
Now, whenever a new technology comes,
it's not long before the bad guys make use of it as well.
An attacker can gain access to
a network that uses AI for threat intelligence.
They can inject traffic into
the data stream with the goal of concealing their tools.
For example, an attack tool
could be disguised as a text editor,
and then, therefore, is not a threat.
This requires knowledge of
a particular AI algorithm that's in
use on the victim system.
In addition, there are a lot of
software packages for
malicious intent that have been seen on
the market that allow attackers to
direct artificial intelligence attacks toward a victim.
These programs are able to make
decisions faster than humans.
When they do get input data back from say, for example,
an Nmap scan, they can more
quickly automate the attack back based on those results.
Virtual or augmented reality.
This is an extended concept of artificial intelligence.
It emulates a real-life environment
with computer-generated sights and sounds.
We have numerous applications for this.
They can be used for training,
providing information to a user
on people or objects within view.
Imagine you need to get someone up to
speed pretty quickly on a given training situation.
A good example of this would be
a military-type situation.
By having a virtual or augmented reality system,
you can walk someone through a given scenario or
a given training situation to
help them pick up the information a lot quicker.
Then on the flip side, we have marketing as
another issue where you have to look at with this.
While we're walking through a given area,
marketing information may pop
up as we pass certain stores.
Or as we look at specific items,
we may see information on our screen that tells
us more details about it and maybe a coupon.
Big data. These are
data collections that are too big for
traditional database tools to utilize.
These are ideally suited to AI
as the larger the data set for AI to study,
the more effective it will be. Deep learning.
This is a type of machine learning that takes
apart the knowledge and then
breaks it into smaller parts.
Complex topics can be broken into
parts that are easier for the deep learning to interpret.
A deep learning system can then decide which parts are
applicable to a given problem
and make decisions based upon those.
IMB's Watson is a good example of this
in action. Quantum computing.
This manipulates data at the atomic level.
Qubits or quantum bits are
the base unit in quantum computing.
Qubits have a value or state of zero or one,
or any value in-between.
They can have multiple states at the same time.
Qubits can become entangled and then the value
can only be observed by collapsing the quantum effect.
The only way to get measurements is to
indirectly entangle two cubits.
This allows quantum computers to
perform many calculations at the same time.
Because of this, quantum computing is especially
useful in breaking RSA and ECC encryption.
Homomorphic encryption.
These are primarily used to share privacy-sensitive data.
It allows for statistical analysis of
data without decrypting the data itself.
The main purpose is to share data,
but at the same time,
keeping the data private.
Components of homomorphic encryption
are private information retrieval or PIR.
This protocol allows for the retrieval of the data
without revealing which specific item is being collected.
We also have secure two-party computation
or secure function evaluation, SFE.
This allows two parties to check
input without disclosing the results.
Finally, we have private function evaluation or PFE.
This describes calculations done by
more than one system but
the function used is only known by one party.
3D printing. 3D printers are
special-purpose printers that build
3D objects rather than printing on flat paper.
Printing is done by adding layers on top of layers
according to a model using
a computer-aided design or CAD software.
This allows for rapid design and
creation of just about anything.
It has been used in health care to create
repair parts quickly and inexpensively.
We saw this at the beginning of the COVID crisis
when certain breathing machines,
the spare parts were not easily available or they were
far too expensive for hospitals to purchase.
A lot of 3D printers or individuals
using 3D printers created
the spare parts to sell to hospitals.
However, it ended up in several lawsuits because
the original company or the manufacturer of
the breathing machines sued
them for trademark infringement.
You can also share plans online with anyone
else and allow them to print whatever your designs are.
This presents a challenge as
even firearms can be printed using 3D printing.
However, I will tell you this from most of
the 3D printing of firearms
if you were to look online about this,
most of these are firearms you would
not want to be holding when you're shooting.
There have been great strides
in the improvement of these,
but most of them just can't handle.
They're just not safe to be
held while they're being shot.
Let's summarize what we went over in this lesson.
We went over the blockchain and its parts.
We discussed artificial intelligence and deep learning.
We went over quantum computing,
homomorphic encryption, and 3D printing.
Let's do some example questions.
Question 1, what technology uses
a peer-to-peer network to distribute
a ledger? The Blockchain.
Question 2, what technology deconstructs knowledge into
a series of smaller parts so
they can be used to interpret data?
Deep Learning. Question 3,
what is the term for when AI can be used against users?
Adversarial artificial intelligence.
Finally Question 4,
which technology allows for the sharing of data for
the purposes of analysis without
actually revealing the data itself?
Homomorphic encryption.
This brings us to the end of Module 2.
I hope you found this module helpful,
and I'll see you in Module 3.