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Impact of IoT
The document discusses the major impacts of emerging technologies like the Internet of Things (IoT) and artificial intelligence. It notes that IoT is connecting over 50 billion devices by 2020 and will have multi-trillion dollar impacts on businesses and cities. Artificial intelligence is also rapidly advancing and being applied across industries like vision, speech, and automation. However, some experts like Bill Gates and Stephen Hawking have expressed concerns about potential risks if artificial superintelligence emerges and humans cannot control it.
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Impact of IoT
- 1. The Impact ofIoT Major Industry Disruptions April, 2017
- 2. PIVIT | TurnSmart karl@piviting.com l www.piviting.com | +1 321 750 5165 O U R C O M P A N Y A B O U T U S I N F O G R A P H I C S C O N T A C T HELPING YOU CREATE ARTIFICIAL INTELLIGENCE INFUSED APPS & SERVICES 2 Applied AI Strategy Product Management From Build To Launch Process Mentoring Invent, Learn, Turn
- 3. PIVIT | TurnSmart karl@piviting.com l www.piviting.com | +1 321 750 5165 Today 3 Internet of Things Innovation driven change Artificial Intelligence
- 4. PIVIT | TurnSmart karl@piviting.com l www.piviting.com | +1 321 750 5165 4 Compute Power Data Speed Connecti- vity Shrinking Size The 21st Century: a Confluence of Accelerating Revolutions by Ray Kurzweil
- 5. PIVIT | TurnSmart karl@piviting.com l www.piviting.com | +1 321 750 5165 5 Inflexion Market insiders rarely predict the expansion duration correctly
- 6. PIVIT | TurnSmart karl@piviting.com l www.piviting.com | +1 321 750 5165 6 13 y1900 1913 HORSE CAR
- 7. PIVIT | TurnSmart karl@piviting.com l www.piviting.com | +1 321 750 5165 7 7 y1993 Expected 900K 2000 (109M) Landline Cell Phone
- 8. PIVIT | TurnSmart karl@piviting.com l www.piviting.com | +1 321 750 5165 8 5 y 2020 20% efficient $15,000 in gas 2,000+ moving parts 2025 95% efficient $1,500 in gas 18 moving parts Greater torque $30k unsubsidized GAS AUTO ELECTRIC CAR
- 9. PIVIT | TurnSmart karl@piviting.com l www.piviting.com | +1 321 750 5165 9 50+ billion connected things by 2020 Inflexion
- 10. Internet of Things “…inretrospect it looks like the rapid growth of the World Wide Web may have been just the trigger charge that is now setting off the real explosion, as things start to use the Net.” Neil Gershenfeld “When Things Start to Think”
- 11. PIVIT | TurnSmart karl@piviting.com l www.piviting.com | +1 321 750 5165 "In the next century, planet earth will don an electronic skin. It will use the Internet as a scaffold to support and transmit its sensations. - Neil Gross, Business Week “ 11
- 12. PIVIT | TurnSmart karl@piviting.com l www.piviting.com | +1 321 750 5165 12 Internetworking of connected devices In order to collect and exchange data So we can improve efficiency, accuracy and reduce human intervention Leading to increased automation in nearly all fields WHAT IS IOT?
- 14. PIVIT | TurnSmart karl@piviting.com l www.piviting.com | +1 321 750 5165 FORBES
- 15. PIVIT | TurnSmart karl@piviting.com l www.piviting.com | +1 321 750 5165 HOW BIG IS THE IMPACT? connected devices by 2020 $15 billion Spent on smart home in 2015 $2 trillion Industrial impact by 2020 $500 B/y Driverless market $100 B/y smart office 50+ billion Spent on IoT in next 5 years $6 trillion biz Lower costs, increase productivity, open markets gov Improve citizens quality of life $1 trillion Smart city market $2 t/y smart factory market
- 17. PIVIT | TurnSmart karl@piviting.com l www.piviting.com | +1 321 750 5165 17
- 18. PIVIT | TurnSmart karl@piviting.com l www.piviting.com | +1 321 750 5165 18
- 19.
- 20. PIVIT | TurnSmart karl@piviting.com l www.piviting.com | +1 321 750 5165 AI IS ALREADY EVERYWHERE
- 21. PIVIT | TurnSmart karl@piviting.com l www.piviting.com | +1 321 750 5165 SPEECH VISUALS AUTOMATED DECISIONS OPTIMIZATION CONTEXT EMOTIONS TRENDS
- 22. PIVIT | TurnSmart karl@piviting.com l www.piviting.com | +1 321 750 5165 Typical ML Cycle Historical source data Predicted outcomes Incident Data Test data Training data 80% 20% Machine Learning Training Machine Learning Models Model weights Quality Metric Predicted outcomes
- 23.
- 24. PIVIT | TurnSmart karl@piviting.com l www.piviting.com | +1 321 750 5165 24
- 25. “I am inthe camp that is concerned about super intelligence. First the machines will do a lot of jobs for us and not be super intelligent. That should be positive if we manage it well. A few decades after that though the intelligence is strong enough to be a concern. I agree with Elon Musk and some others on this and don’t understand why some people are not concerned.” Bill Gates
- 26. “Success in creatingAI would be the biggest event in human history,…” “Unfortunately, it might also be the last, unless we learn how to avoid the risks. In the near term, world militaries are considering autonomous-weapon systems that can choose and eliminate targets.” “…humans, limited by slow biological evolution, couldn’t compete and would be superseded by A.I.” Stephen Hawking
- 27. PIVIT | TurnSmart karl@piviting.com l www.piviting.com | +1 321 750 5165 27 Partnership on Artificial Intelligence to Benefit People and Society
- 29. PIVIT | TurnSmart karl@piviting.com l www.piviting.com | +1 321 750 5165 O U R C O M P A N Y A B O U T U S I N F O G R A P H I C S C O N T A C T 29 Thank You Get in touch with us! 29 Karl Seiler | President PIVIT – TURN SMART karl@piviting.com Piviting.com @pivitguru +321 750 5165
Editor's Notes
- #2 Thanks to the folks at SCTC for having me here to talk to you today. I hope to give you some insights into the big change impact coming with the Internet of Things. Where it is headed and why you will want to pay attention.
- #3 I am Karl Seiler, president of a consulting company PIVIT. PIVIT helps organizations adopt applied Artificial Intelligence, technical product build through launch, and lean/agile methodology. I am currently helping launch a new company called ZONTAL. I am VP of AI at Big Data Florida, and co-founder of Central Florida Machine Learning
- #4 Today I am going to quickly talk about the acceleration of innovation and examples of technology driven disruptions, IoT as representative of one such disruptive change. The Artificial Intelligence revolution helping make IoT work smart.
- #5 We live in a world full of technical innovation that is accelerating change. This increasing rate of change is impacting civilization as we know it across many fronts. More compute power at a lower cost. The rise of connectivity, aka the Internet’s reach. Increases in how fast we can more large amounts of data across that connectivity. The smaller physical scale at which engineering is able to work. To me this means in essence that you live in a sea of change never seen before. That your children and grandchildren will live a life unimaginable to us today.
- #6 Some examples of disruptive change. We watch for when a technology transitions from a try and fail state of infancy, to an inflexion point that changes the economics and capability and where the existing business models are significantly disrupted. Market insiders rarely see it coming or call the timing right. Often and increasingly the expansion phase is surprisingly swift. Leading quickly to saturation & maturity.
- #7 An example. From a world of mostly horses to mostly cars? Duration of the expansion phase? Guess? 13 years
- #8 A world where the landline telephone dominates to cell phone dominance (varies per country) Expansion phase ? Only 7 years
- #9 One more. An inflexion you are currently in. Gas car to electric car. Been in the works for a long time. But the numbers now make it economically inevitable. Especially in the most populated countries. Projected transition 5 years. Electric has more torque, massively more efficient, 1/10th the cost, orders of magnitude less moving parts. The cost curve is rapidly driving right down though the heart of the gas car’s benefits model.
- #10 And here is the inflexion we are in over the next few years as the IoT adoption curve bends up. As one metric we anticipate 50 billion internet connected devices in the next 3 years. Zooming up from there.
- #11 So what is this new beast! Neil’s quote here portends that the Internet was just a triggering event, a catalyst for IoT
- #12 One way to think about IoT is as an electronic planetary sensory skin built on the scaffold of the Internet
- #13 What is IoT? It is the internetworking of connected devices, in order to collect and exchange data. So we can improve efficiency, accuracy and reduced human intervention. Ushering in revolutionary automation in nearly all fields.
- #14 Why does it matter so much. Basically it allows us to see and respond to patterns we could not ever see before. Here is a new pattern. A toddler location tracker, voice recorder and language processor coupled to analytics. This is a pattern of how and when a child learns to talk overlaid onto the map of their apartment. Leading to the discovery that context matters more than repetition in language acquisition. The peak is the location where this child learned the most words fastest. The kitchen.
- #15 What is impacted by IoT Manufacturing is number 1 – automation. Supply chain optimization IT – voice Retail - location Finance and insurance – wellness and driving Healthcare
- #16 Some data: 20-50 billion connected devices by 2020. $6 trillion to be spent on IoT in the next 5 years For companies : lower costs, improve productivity and new markets For Governments better quality of life $15 billion was already spent of smart home products in 2015 An industrial impact of $2 trillion $500 billion per year in driverless vehicle $1 trillion per year in the smart city $2 trillion per year in the smart factory And $100 billion per year in the smart office Big stuff
- #17 Lets explore the working parts of IoT. It is a layer cake. From the bottom up. 1 - The things, devices, sensors and controllers. Low power, small, compute strong, sensitive, ubiquitous 2 – connectivity. The hop. Bluetooth, Wi-Fi, hubs, etc. 3 – The edge. Local processing and analysis for faster reaction time that can not cycle up to the cloud and back in time. The fly by wire car is the best example. Also, called fog computing. 4 – data accumulation – where does the data go, cloud, NAS, SAN, public / private models, hybrids, elastic capacity 5 – Data abstraction – your data, my data, cohorts of data, control, access, security of the stored asset, backup recovery, retention policies 6 – the application layer, trending, categorization, classification, machine learning, decision automation, control commands 7 – Collaboration and processes – integration with business processes, transactions, other systems integration across the enterprise, etc.
- #18 Here is a view of what kinds of things IoT sensors can sense. Light, movement, power, flow, force, position, temperature, moisture, sound, chemicals. Sensors are a powerful extension of our own senses. Coupled with data and pattern analysis, we can learn deep things from small sources. Did you know a motion detector (like a fitbit) can tell if you are depressed?
- #19 What are the problems with IoT? Here are some #1 security More vulnerability More data in flight More loss of privacy #2 automation accidents #3 data volume #4 ethical stumbles
- #20 So lets talk a little about the rise of AI as it relates to IoT.
- #21 AI is not some future. It is here. Now and you all use it every day. AI recommends products, music, helps search, trades on the market, builds things, is on your wrist, is starting to drive, fights your battles, reviews your taxes and calculates your credit risk.
- #22 AI covers a broad sweep of technologies. It is algorithms that mimic human thinking. To power IoT most AI current activity is in machine learning platforms, natural language recognition, voice and audio pattern analysis, biometrics pattern detection, and text analytics.
- #23 How do machines learn. Rather how do we train machines. The key is data. Some historical reference set is split 80 / 20 for training data and test data is set aside. A model is selected and tuned using data science and or training algorithms. Creating a model. Real incident data is run through the model, results are predicted. Results are measured against the test data for quality. New model weights are adjusted. The model is updated. Testing and model tuning cycles until good enough.
- #24 With machine learning success is less about the greatest algorithm and is more about the amount of data available to train from and how good that data is. Data science and data wrangling is mostly about understand good versus bad data and knowing how to work with model options to converge on success.
- #25 Deep Learning Nets is a breakthrough approach yeilding impressive results. It is a layered approach that learns features. Given an image low layer might learn to recognize a feature such as a line. A higher layer may learn to see groups of lines as a eye, nose, mouth. A higher layer may learn that groups of face parts means a human face. A higher layer may learn your face versus my face versus the bad guy’s face.
- #28 Given the rising concerns about super intelligence, new organizations are rising to address how to manage, contain and make safe these capabilities. One such is Elon Musk-backed Open AI Another is the partnership on AI to benefit people and society So with every great leap forward, we often stumble, cause problems, clamp down, and eventually figure it out. We will stumble. We will not slow down because of the economic imperatives.
- #29 Lastly I recommend that when you think about IoT, do not think about your sports gear. That is nice and all, but the big impact will be in the combined resources represented in large part by the modern city. The smart building aggregates up in part to the push for smart cities. Cities start to vie with other cities to attract talent and business due to the optimized transport, low cost of energy, integrated health access systems, responsive security systems, flexibility designed in for events, etc.
- #30 Welcome to the chaning world. Thanks You all for listening. Again, I am Karl Seiler from PIVIT. Hope to hear from you.