break free, taryn southern’s ai-assisted music...
TRANSCRIPT
Artificial IntelligenceBy George Naugles for BUS550 with Minder Chen, Ph.D.
Artificial Intelligence*Machine Learning *Deep Learning*Neural Networks*Electronic Brains..
https://thelastdriverlicenseholder.com/2017/11/07/historic-milestone-waymo-to-become-first-company-to-deploy-autonomous-car-fleet-without-driver-on-board/
(c)Copyright George Naugles, 2017, all rights reserved.
Artificial Intelligence
What?
So What?
Now What?
What?
https://www.youtube.com/watch?v=ILsA4nyG7I0
Chips + Wires + Fiber + Code Mimic Brains
...with Neural Nets, big versions of this
Taking you where you ask . . .
...still working on truck-evasion while parked….sorry!!!
Fresh Direct feeds millions with AI, powered by gaming chips!
Elemental AI
● Multi-core microprocessors like the 8 core NVDA chip in Playstation 4,
● ASICs=Application Specific Integrated Circuits,
● FPGA’s = Field Programmable Gate Arrays, ...all with “divide and conquer” coding/processing approaches (p.100)
AI in John Gallaugher’s Information Systems: A Manager’s Guide to Harnessing Technology● Remember the NVDA 3D graphics chips so important to VR and AR?
Now there is a rise of similar but better chips specialized for machine learning and AI, enabling super-complex applications like blockchain tech and state of the art security. (IS:MGtHT p.5)
● “FreshDirect model crushes costs that plague traditional grocers...Artificial Intelligence software, coupled with over 7 miles of fiber optic cables and sensors supports everything from baking the perfect baguette to verifying orders with 99.9% accuracy.” (p.27)
● (IS:MGtHT=our class text Information Systems: Manager’s Guide to Harnessing Technology)
AI Value PropositionAI = neural nets + deep machine learning,
...enable machine learning to do any task for which high-quality data can be gathered to enact a process of continuous improvement
=>Business Process: collect and organize lots of data in formats your current and future AI computer can process,
...Which data? Any data you think may help your AI build a predictive model for your biz, subject to cost constraints.
Neural Networks, this instrument can “learn”
Implication: Collect, Organize, & Mine AMHQDAP
● AMHQRDAP = As Much High Quality Relevant Data As Possible
● Today’s AI software and hardware can write its own models by looking at reams of unstructured data to find predictive patterns and build predictive models from all the data we can feed them
2018-2028 we’ll manage neural nets,both human and machine
...catering to YELP, FB, & TWTR Reviews in addition to price per share, which in turn is affected by data mining that taps real-time mobile internet reviews...
High quality data + AI guide our decision making as managers may determine which “winner takes all” in AI-powered 21st century business. Super-human customer-satisfaction capabilities capable of creating unprecedented customer loyalty and CLV characterize successes like AMZN, NFLX, GOOG, and BABA.
Employee NetBrainsFunction with neural networks classified as default, reward, affect, and control.
There are AI applications you can, as a manager, add to your team in order to gain powerful insights from data your human employees would find overwhelming. https://hbr.org/2013/07/your-brain-at-work
What are some inputs AI currently helps us process?
1. Artificial Vision data consisting of 25 to 30 frames per second.2. Quantities of data too great for any human to process.3. Overwhelming quantities of high quality data with predictive value that AI can
discover and use to inform managers about the likely future with or without specific decisions . . . to select and offer projections with unprecedented insight.
4. An increasing number of Medical Diagnosis tasks and procedures.5. Case outcomes for judges where attorneys help clients decide strategy.
Artificial Vision, which one is real?
Argus 2http://www.secondsight.com/
Superhuman visual recognition enabled by AI
Increasing computational capability for computer,
Microsoft’s Visual Studio LifeCam with facial recognition
AI is what scientists are using to solve the most vexing problems, like climate models and projecting climate change
A I
https://www.technologyreview.com/the-download/608726/climate-change-research-is-getting-a-big-dose-of-ai/
AI robots teach themselves hand-eye coordination(start at 8:55)
GoogleBrain is designing specialized computers
Accelerates neural net computations at least one order of magnitude. Does Linear Algebra extremely well.
“Machine Learning Is . . . like making good wine”Good data, like good grapes = essential
Sebastian Thrun, Stanford Research Prof, founder of Udacity, team member for GoogleStreetView, GoogleGlass, Google GoogleX, Self-Driving Car Challenge, etc.
Katy Mallone, Physicist, Udacity Machine Learning Specialist
GOOD DATA MAKES GOOD MODELS
Like good grapes make good wine
In Business, Intelligence involves...
Generalized Learning, Reasoning, Problem Solving, & Language Comp.
Artificial Intelligence involves:
Machine Learning, computer vision, natural language processing, robotics, pattern recognition, & knowledge management for self-driving car control, recommendation engines, and optimization of just about any operation.
Strong AI simulates the human brain, while weak AI immitates human behavior in ways that are not biomimicry, ...like DeepBlue a chess playing AI that considers thousands of moves before choosing one. Watson is more advanced, determines x% confidence level of answer. Google’s AlphaGo was even more advanced.
What’s better than Watson?Google’s DeepLearning and GoogleBrain immitate brain structure, with artificial neurons, and neurological biomimicry to achieve greater processing capability, more creative capabilities, and more autonomy to learn how to perform as humans would.
Neural Networks = subsite of machine learning.
Business Decision-making based on data has depended on management information system and managers, AI has the potential to assist managers, and possibly replace them, making superhuman decisions. There seems to be an AI arms race in a growing number of markets, allowing leaders to out-compete more primitive conventional firms with superhuman performance superiority.
Emotional connection, creativity, and customer intimacy are seen as exclusively human realms for now, yet AI has the potential to outperform in those areas as well.
Major Issues of Emerging AI Technologies
*Modernizing to compete: AI Arms Race between Google, Intel, IBM, Amazon, Netflix and its competitors, and a wide range of start-ups writing code and creating optimized AI hardware.
*Google Cloud Platform and Amazon Web Services both offer Deep Learning Machine Learning Artificial Intelligence mainframe capabilities that allow any start up to spin-up thousands of instances on seconds’ notice. This dramatically decreases barriers to entry for entrepreneurial startups using AI.
*Will these AI applications escape the control of their designers, self-replicate on the internet, and create new software that has negative effects for one or more internet or mobile technology users in a future world full of IoT?
So What?
How can we make money from superhuman artificial
intelligence?
Can you find Artificial Intelligence in this StarTrek image? Can humans hope to compete with AI?
If we cannot beat AI’s, how can we hire them and cooperate?
350 AI researchers say MIT’s McAfee is wrong.Some of their projections are actually coming true faster than expected:
Language translation 2024,
Essay writing 2026,
Truck-driving 2027,
Retail work 2031,
Writing a best-seller 2049,
Surgery 2053, and all human jobs wil be automated within 120 years.
Deep Neural Networks build layers of abstraction as part of learning processComplexity increases as the process continues.
….Tensorflow is now open sourced, which reveals part of GOOG’s Strategy of providing the most economical option for high performance AI computing so they can know more than anyone about operations to execute and maintain the required processes, running on GOOG’s Tensor Processing Units.
Opthalmologists agree 60% of the time.
Same Opthalmologists only agree with self 65% of the time.
GoogleBrain has a machine learning model that performs on par or slightly better than (slightly more consistently than) opthalmologists.
AI Organization (Business Processes)● Collect high quality “clean” data to feed AI applications that find
patterns and create custom predictive models to fit the available mined data.
● Introduce that data to machine learning modules
● Give the modules lots of data and iterations to learn, predict, test predictions, and improve iteratively
● Let the module improve itself continuously to get better and better at its tasks
● Apply to tasks that involve frequent, repetitive implementation of expertise feeding a clean data collection process
AI for HR? With its relentless focus on facts, AI
seems to overcome supervisors’
prejudices, but it can have its own biases, such as favoring job candidates who have
characteristics similar to those the software has seen before. Automated decision-making may also tempt
managers to abdicate their own judgment or justify bad decisions that would have benefited from a human
touch.
Another caveat: These systems are fairly new, and we really don’t know yet
whether they make decisions that are as good as
or better than human managers.
And it would be difficult to devise a
foolproof way to test that.
Hires who can interact effectively with the AI modules, cleaning input data, scrutinizing and communicating results with team members in the most productive ways.
Who are patient, intelligent, and open-minded
Who are able to team with computers running AI + people :J
What new hires can best implement AI?
AI Management (Decisions)Which AI provider? AWS, GOOG, MSFT, IBM, AAPL, or younger co’s?
Any decision involving data is fair game for AI, which can beat any human at Go now, and at everything 120 years from now. Our decisions must be made cooperatively with AI and our ability to function effectively with teams doing what AI is not yet capable of doing is key.
Interviewing/Hiring Decisions Made with AI support through hireMya.com, paradox.ai, entelo.com’s Envoy, leveraging predictive analytics and smart automation technology to manage email outreach and engagement, and streamline your efforts to recruit, screen, and setup interviews to hire new staff.
AI to screen loan applicants and spending initiatives...
AI Strategies*Focus AI efforts on areas with large quantities of high quality data
*Target tasks that involve repetitive implementation of expertise and also sensors that exceed high value professionals’ human sensory capabilities, like opthalmologists, for example
*Google strategically implemented an interesting strategy engineering its own TPU chip to more optimally run Tensorflow and beat Intel in its release of an AI chip.
*Intel implemented the strategy of releasing a USB3.0 device to add AI processing capacity to people’s existing computers, for an ultra-low-cost hardware solution ($70).
2016-2017 AI ApplicationsHANA, SAP’s cloud platform: Walmart uses HANA to process transactions from 11,000 stores within seconds, controlling back office costs by consolidating processes and resources needed to handle the work.
DMWay for predictive analytics
Domo collects data from 3rd party apps, adding insights and context to business intelligence; pulling data from Salesforce, Square, Facebook, Shopify, etc...used by Mastercard, eBay, SAB Miller, Honest Co., etc. Univision claims to use Domo with connectors for GoogleAnalytics, Facebook, and Adobe Analytics to maximize value from programmatic advertising.
Apptus - AI in Sales Enablement for automation of merchandising based on a predictive understanding of customers.
Cloudera for deep learning adept at prediction and anomaly detection.
Additional AI ApplicationsAvanade, Accenture from Microsoft
GE’s Predix Platform as a service (PaaS) with data analytics to drive better outcomes, alerting owners when parts need repairs, analayzing enormous amounts of data.
Pitney Bowes AI software written to complement Predix, achieved 20% improvement in operations efficiency.
Siemens offers MindSphere to monitor machine fleets
AMZN
Successful Examples of AI in Business: Google
AI Business/IT Solutions
Artificial Intelligence at Google
IBM Using AI to detect fraud & diagnose with Watson
Safer Payments and other IBM Applications, helping analysts understand what to look for.
Medical Diagnostic Support AI
Now What?
AI Business Challenges/Problems/OpportunitiesChallenges: Established companies may lack the organization and data-accessibility necessary to make efficient use of AI and Machine Learning. On the other hand large companies should ideally have a tremendous advantage if they can feed high quality data from their large number of transactions into machine learning modules that help them improve Customer Intimacy, Operational Excellence, and Performance/Product Superiority...conducive to a “winner take all” stratification of markets.Problems: AI processes’ ability to self-modify and self-replicate is evolutionary, creating the potential for extremely pervasive and powerful technology to do things the original designers didn’t foresee or intend. It is indisputably capable, so companies must adopt it in order to compete, yet they may be at its mercy if they lose control of it.Opportunities: Companies who efficiently implement AI can dramatically increase their efficiency and effectiveness in virtually every aspect of their business model. Logistics, agriculture, human resources, resource management, and more.
ChipsWhich laptop can you buy that has the best AI capabilities?
IntelIntel AI applications
11/14/17 Belgian Neural Network Photonic Chip
Why not add superhuman intelligence to…your brain?
Or did you want to let your rival try it first?
Is AI benign or does it have the potential to harm?
What about iRobot, ExMachina, Uncanny, and Terminator?
AI is Harmless, Musky naysayer!!
Will AI technologies experience emotional pain because people disregard their feelings and treat them as non-feeling objects?Will AI technologies seem problematically unauthentic and frustrating from customer’s or staff’s point of view? Will they be annoying?
Will AI systems escape our control, harming, killing, or imprisoning us as collateral damage on their path to self-actualization?
What Next?Will AI systems compete with one another for our attention, etc.?
Will AI systems compete in ways that may harm us, our job(s), or our interests? Will AI and its nanotechnology cousins seduce us or otherwise invade our bodies or minds in profound ways we are unable to refuse or control?
How can we protect ourselves from the delusion of thinking AI can fulfill our interpersonal needs?
How can we maintain balance in our social lives when AI automation replaces so many of the experiences where we used to meet people? E.g. online grocery order/delivery, online shopping, etc.
When is exclusivity and confidentiality too important to trust AI?
“Her” is in his cell phone, computer, internet ...
Will AI control us? … does it already?
● Most managers justify their power based on expertise and legitimacy of position. What will happen when AI systems’ expertise far outperforms our own?
● How will managers justify their higher salaries and their authority to direct operations?
● To what extent will AI displace managers, i.e. you and me?
● How can we use AI to most effectively compete?
Now What?What AI have you seen or experienced?
What data would you like to feed to AI to improve? _________...in 2018-2023?
Thanks for your attention! Here are your review Q’s:Q1)Is machine learning:A)Humans training on a variety of machines.B)Machines learning how to act like Valley Girls.C)Introducing high quality data to computers that “train” on the data to optimize safety, performance, or other parameter sets.
Q2)Two companies who have designed chips ideal for Linear Algebra and other Artificial Intelligence Processing include:A)Netflix and WalmartB)Chips Ahoy and DoritosC)Google and Intel
Q3)Stanford Professor and Udacity founder Sebastian Thrun made an analogy between machine learning input data and…A)stacks of crackers because stale crackers are like old data that may misinform AI applicationsB)grapes for making wine because good grapes, like good data, enable quality machine learningC)grass fed beef because most beef is not grass fed and contains lots of fat, similar to unnecessary data
https://www.youtube.com/watch?v=XUs6CznN8pw
Additional Sourceshttps://newsroom.intel.com/news-releases/intel-capital-fuels-innovation-60-million-new-investments-15-data-focused-startups/
https://www.wsj.com/articles/how-ai-is-transforming-the-workplace-1489371060
https://newsroom.intel.com/press-kits/artificial-intelligence/
http://www.electronicdesign.com/embedded-revolution/intel-s-ai-chip-available-usb-stick
MIT Deep Learning Image Recognition, Speech Recognition, + Magical Products
http://www.asimovinstitute.org/neural-network-zoo/
https://ucbrise.github.io/cs294-rise-fa16/prediction_serving.html
http://www.asimovinstitute.org/neural-network-zoo/
https://www.youtube.com/watch?v=S_3N_aFLiH4;
Thanks for Watching!!
AI in John Gallaugher’s Information Systems: A Manager’s Guide to Harnessing Technology (continued)
P.115, p.121, p.130, p.147, 262, 356, 363, 373, 378-379, 384, 447, 473, 482.
Extra slides below here, probably won’t use them
