2018 05 hype lightning talk
Download as PPTX, PDF2 likes1,985 views
The document discusses emerging technologies including quantum computing, artificial intelligence, machine learning, blockchains, the internet of things, cloud computing, edge computing, and data analytics. While these technologies show promise, the document cautions against hype and notes that many technologies do not yet work as described or have practical applications. The author offers experience supporting genomics and building IT infrastructure and is available for consulting work.
2018 05 hype lightning talk
- 1. Quantum AI Machine Learning Blockchains on the IoT Cloud Data Ocean Turning Hype Into Reality Chris Dwan May 31, 2018 chris@dwan.org https://dwan.org @fdmts
- 2. Who do you have here? A “Cray 2” supercomputer on display at the national cryptographic museum. This model of system were the fastest computers in the world until 1990. By one measure of raw performance, the Cray 2 is comparable to a single iPhone Academic background in AI / neural nets (before they worked well) ~20 years supporting genomics for computing, data storage, and similar topics 9 years with BioTeam (consulting company) Designed and led implementation of IT infrastructure for the NY Genome Center. Directed Research Computing (and, briefly, all of IT) at the Broad Institute. Building my own consulting company (https://dwan.org)
- 3. Data: The new oil* Data Base: Structure, queries Data Warehouse: All the data in one place. Limited integration. Data Mart: Serve up warehoused data to users (Shiny counts) Big Data: Volume, Variety, Velocity Data Lake: Data warehouse, but designed for in-situ analytics Data Ocean: A data lake, for cromulently embiggened data! Data Commons: When the benefits of sharing data outweigh the competitive instinct to horde it Data Biosphere: A data commons, but for the cool kids An immature ‘tyrant flycatcher. Needs a data mart, because it doesn’t know R or Linux yet. Hype-o-meter Impact-o-meter
- 4. Cloud Public cloud: AWS, Azure, GCS, plus a bunch of wannabes. Private cloud: Cloud services on gear you own. Fog computing: On premises equipment used for cloud stuff. It’s fog because that’s a cloud that’s close to earth. Get it? Enterprise cloud: IT trying desperately to align with a cloud strategy by changing the labels on the powerpoint. Multi cloud: Mostly using Amazon, but with Azure for business stuff and Google for that one weird trick. Using the right tool for the right job. Hybrid cloud: Using your own stuff for a particular service, but bursting to Amazon for extra capacity during peak usage. Look up “Pizza as a Service.” “On premises,” or “legacy,” carrot cake still has a place, even in homes with a baker-as-a-service strategy. Hype-o-meter Impact-o-meter
- 5. Machine Learning (ML) Algorithms that optimize and tune based on large amounts of data These have been around for a very long time (KNN and Linear Regression are totally ML). Algorithm innovations (deep neural nets), plus ubiquitous big data, plus improvements in computing, storage, network, and software. Killer apps everywhere in image recognition, natural language processing, clustering, categorization Hype-o-meter Impact-o-meter A ‘swan pink yellow’ columbine flower. Identifying objects in images is machine work now.
- 6. Artificial Intelligence (AI) Distinguished (for me) by autonomous behavior and clever-looking behavior in the face of unanticipated situations. No requirement that “intelligent” mean “like a human.” Machine learning algorithms are a great (but not the only) way to create AI systems. Beware “bread machine AI.” Hype-o-meter Impact-o-meter Getting there! My cat shows surprising intelligence despite having a brain the size of a walnut
- 7. Artificial Intelligence (AI) Distinguished (for me) by autonomous behavior and clever-looking behavior in the face of unanticipated situations. No requirement that intelligence be human style. Machine learning algorithms are a great (but not the only) way to build AI systems. Beware “bread machine AI.” Hype-o-meter Impact-o-meter Getting there! My cat shows surprising intelligence despite having a brain the size of a walnut
- 8. Internet of Things (IoT) All sorts of devices on the internet, with sensors and embedded operating systems. Huge issues around security, privacy, reliability: Dishwasher companies have no idea how to patch software in the field. Grouchy old me says: We used to call these ‘embedded systems! My phone is IoT!’ Not quite accurate, but not fully wrong either. Next wave source of big data. Hype-o-meter Impact-o-meter Lightbulbs used in botnets! Would this sculpture be better if it ran Debian Linux?
- 9. Edge Computing Machine Learning plus Internet of Things = unexpected and powerful use cases. Example: Optical character recognition in lightweight cameras allows upload only of license plate text, rather than whole images, “preserving privacy.” Hype-o-meter Impact-o-meter Please stop mailing hard disks. It’s a terrible way to move data.
- 10. Edge Computing Hype-o-meter Impact-o-meter Please stop mailing hard disks. It’s a terrible way to move data. Machine Learning plus Internet of Things = unexpected and powerful use cases. Example: Optical character recognition in lightweight cameras allows upload only of license plates, rather than whole images, “preserving privacy.”
- 11. Blockchain ”The clown car of our industry in 2018” • Distributed ledger: trustworthy data / records without a central authority. • Self executing contracts: Shared, trustworthy code to operate on that data. • Initial Coin Offerings: massively accelerated (and deregulated) way to set monetary value on a data ecosystem. Amazing possibilities in permission / consent management. When I make snarky comments on LinkedIn, people ask if they can invest. Hype-o-meter Impact-o-meter The angel weeps because there are some really compelling use cases for blockchain, but the hype is deafening.
- 12. Quantum Computing A totally different framework for computing. Subatomic particles are ‘entangled’ and maintained in a ‘coherent’ state while constraints are imposed on their relationships, allowing near-instant solutions to formerly intractable math problems. Going to blow up large complicated search / optimization technology: Cryptography, comp. chem, docking, … Serious engineering issues remain, including bit level error rates that are hard to even explain. Bitcoin founder’s account is considered a ‘quantum canary.’ Hype-o-meter Impact-o-meter Sadly, the quantum doughnuts will not be ready for another few years
- 13. Quantum AI Machine Learning Blockchains on the IoT Cloud Data Ocean It doesn’t work yet It sure looks clever SHOW ME THE MONEY Why did it do that?
- 14. Quantum AI Machine Learning Blockchains on the IoT Cloud Data Ocean It doesn’t work yet It sure looks clever Now you’re just making stuff up. Wait, like on a thermostat? IoT is literally the opposite of cloud. SHOW ME THE MONEY Why did it do that?
- 15. Call to action Don’t believe the hype Email me if you need a hand with anything. chris@dwan.org https://dwan.org @fdmts