Emerging Technologies

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Time
8 hours 20 minutes
Difficulty
Advanced
CEU/CPE
9
Video Transcription
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>> Emerging technologies.
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The learning objectives for this lesson are
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to describe blockchain concepts,
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to explain artificial intelligence and augmented reality,
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and to discover big data,
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deep learning, and quantum computing.
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Let's get started. Blockchain. You're probably
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are familiar with this at
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some level because you've heard of Bitcoin.
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Bitcoin is the most well-known example
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of the blockchain in use.
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But the blockchain can be used for
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many different types of problem-solving.
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The blockchain at its basic level is an expanding list of
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transactional records that are secured by cryptography.
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Each record is known as a block,
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and then they're all connected together in the chain.
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Each block is hashed and
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the hash value of the previous block is included with it.
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This way, each block
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validates the next block in the chain.
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One thing to keep in mind is that the blockchain is
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a public ledger that is
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distributed across peer-to-peer networks.
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Back to Bitcoin, a lot of people
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think that Bitcoin is anonymous.
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Now we might not know who owns a specific wallet address,
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but every transaction that wallet has ever done in or
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out is available for everyone to see on the blockchain.
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This is something that's not commonly understood.
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But since it is a public ledger,
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anyone can view the records.
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Those wallets again, are not anonymous.
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We don't know who owns it necessarily,
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but we do know what that wallet has been doing.
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Secure multi-party computation.
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This is distributing
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computations across multiple systems.
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But the key is that no individual system
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is able to read the data of
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the other parties in this grouping.
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This allows us to solve
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complex problems without compromising
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the privacy of the data.
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Some examples of when we would use this maybe would be
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DNA research or research into patient data.
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Distributed consensus.
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A distributed or decentralized system where
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all the systems come to
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an agreement for a specific computation.
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We're doing this to maintain
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the overall integrity of a distributed system.
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Because we know that some of the systems that are
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included in this might be malicious,
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the distributed consensus takes
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a vote from all of the systems about the data.
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The value with the most votes
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is the one that gets accepted.
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Artificial intelligence.
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This is the science of creating
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computing systems that can
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simulate or demonstrate intelligence
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that's similar to the level of a human.
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Machine learning uses algorithms to parse data,
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then develop strategies for using that data.
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Machine learning can modify the algorithms and
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make gradual improvements in
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its ability to make decisions.
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Adversarial artificial intelligence.
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Now, whenever a new technology comes,
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it's not long before the bad guys make use of it as well.
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An attacker can gain access to
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a network that uses AI for threat intelligence.
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They can inject traffic into
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the data stream with the goal of concealing their tools.
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For example, an attack tool
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could be disguised as a text editor,
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and then, therefore, is not a threat.
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This requires knowledge of
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a particular AI algorithm that's in
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use on the victim system.
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In addition, there are a lot of
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software packages for
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malicious intent that have been seen on
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the market that allow attackers to
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direct artificial intelligence attacks toward a victim.
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These programs are able to make
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decisions faster than humans.
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When they do get input data back from say, for example,
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an Nmap scan, they can more
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quickly automate the attack back based on those results.
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Virtual or augmented reality.
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This is an extended concept of artificial intelligence.
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It emulates a real-life environment
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with computer-generated sights and sounds.
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We have numerous applications for this.
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They can be used for training,
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providing information to a user
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on people or objects within view.
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Imagine you need to get someone up to
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speed pretty quickly on a given training situation.
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A good example of this would be
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a military-type situation.
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By having a virtual or augmented reality system,
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you can walk someone through a given scenario or
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a given training situation to
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help them pick up the information a lot quicker.
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Then on the flip side, we have marketing as
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another issue where you have to look at with this.
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While we're walking through a given area,
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marketing information may pop
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up as we pass certain stores.
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Or as we look at specific items,
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we may see information on our screen that tells
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us more details about it and maybe a coupon.
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Big data. These are
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data collections that are too big for
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traditional database tools to utilize.
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These are ideally suited to AI
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as the larger the data set for AI to study,
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the more effective it will be. Deep learning.
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This is a type of machine learning that takes
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apart the knowledge and then
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breaks it into smaller parts.
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Complex topics can be broken into
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parts that are easier for the deep learning to interpret.
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A deep learning system can then decide which parts are
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applicable to a given problem
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and make decisions based upon those.
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IMB's Watson is a good example of this
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in action. Quantum computing.
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This manipulates data at the atomic level.
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Qubits or quantum bits are
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the base unit in quantum computing.
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Qubits have a value or state of zero or one,
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or any value in-between.
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They can have multiple states at the same time.
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Qubits can become entangled and then the value
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can only be observed by collapsing the quantum effect.
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The only way to get measurements is to
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indirectly entangle two cubits.
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This allows quantum computers to
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perform many calculations at the same time.
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Because of this, quantum computing is especially
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useful in breaking RSA and ECC encryption.
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Homomorphic encryption.
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These are primarily used to share privacy-sensitive data.
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It allows for statistical analysis of
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data without decrypting the data itself.
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The main purpose is to share data,
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but at the same time,
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keeping the data private.
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Components of homomorphic encryption
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are private information retrieval or PIR.
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This protocol allows for the retrieval of the data
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without revealing which specific item is being collected.
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We also have secure two-party computation
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or secure function evaluation, SFE.
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This allows two parties to check
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input without disclosing the results.
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Finally, we have private function evaluation or PFE.
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This describes calculations done by
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more than one system but
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the function used is only known by one party.
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3D printing. 3D printers are
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special-purpose printers that build
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3D objects rather than printing on flat paper.
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Printing is done by adding layers on top of layers
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according to a model using
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a computer-aided design or CAD software.
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This allows for rapid design and
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creation of just about anything.
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It has been used in health care to create
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repair parts quickly and inexpensively.
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We saw this at the beginning of the COVID crisis
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when certain breathing machines,
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the spare parts were not easily available or they were
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far too expensive for hospitals to purchase.
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A lot of 3D printers or individuals
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using 3D printers created
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the spare parts to sell to hospitals.
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However, it ended up in several lawsuits because
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the original company or the manufacturer of
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the breathing machines sued
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them for trademark infringement.
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You can also share plans online with anyone
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else and allow them to print whatever your designs are.
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This presents a challenge as
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even firearms can be printed using 3D printing.
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However, I will tell you this from most of
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the 3D printing of firearms
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if you were to look online about this,
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most of these are firearms you would
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not want to be holding when you're shooting.
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There have been great strides
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in the improvement of these,
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but most of them just can't handle.
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They're just not safe to be
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held while they're being shot.
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Let's summarize what we went over in this lesson.
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We went over the blockchain and its parts.
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We discussed artificial intelligence and deep learning.
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We went over quantum computing,
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homomorphic encryption, and 3D printing.
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Let's do some example questions.
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Question 1, what technology uses
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a peer-to-peer network to distribute
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a ledger? The Blockchain.
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Question 2, what technology deconstructs knowledge into
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a series of smaller parts so
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they can be used to interpret data?
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Deep Learning. Question 3,
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what is the term for when AI can be used against users?
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Adversarial artificial intelligence.
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Finally Question 4,
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which technology allows for the sharing of data for
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the purposes of analysis without
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actually revealing the data itself?
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Homomorphic encryption.
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This brings us to the end of Module 2.
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I hope you found this module helpful,
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and I'll see you in Module 3.
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