Potentially creepy human-computer interactions in the future of the consumer IoT. Lots of raw data need to be analysed and are represented as result of machine learning exercises. However, consumers are likely scared of probabilities. How can UX address these issues?
My talk at Smart IoT London. About adding 'context' for data analytics in the consumer IoT, touching on machine learning, hidden variables, and UX/UI of communicating probabilities.
My keynote from the Location Intelligence session at Geo-IoT World in Brussels in May 2016. How location is one of many important context variables in the interpretation of sensor data.
O'Reilly Webcast: Organizing the Internet of Things - Actionable Insight Thro...Boris Adryan
Traditional machine-to-machine (M2M) uses the internet to replace what was previously achieved through a wire. The challenges for IT are not much different to any other implementation of a prescribed business model.
But how are we going to leverage the connectedness of devices in the consumer Internet of Things (IoT) in a world in which every individual may show a different degree of technology adoption? Not everyone has the connected Crock Pot! The challenges are manifold, and while in 2015 we are still arguing about technical standards that hinder communication of things across platforms, the looming challenges of data integration are even more significant.
Even if all devices e.g. in the connected home of the future are going to speak one language, how are we generating actionable insight from the available information according to the users' need? How do we determine the appropriateness of action? An empty fridge might be alarming, but should we inform the user of an impending hunger crisis if the door hasn't been opened in a week, the heating system is set to low, the car is parked at the local airport? Draw your conclusions!
Ontologies organize things and establish their relationship to each other. They can be used for knowledge inference. For example, a car is a means of transport and ultimately an indicator of absence or presence. Some scientific domains are already making extensive use of ontologies to deal with vast amounts of information. The Gene Ontology (GO) has over 40k interlinked terms that describe cell and molecular biology. For every biological entity on that scale, we can ask: Where is it? What is its function? What process is it involved with? Benefitting from substantial government funding (in the range of > $40M from the NIH since 2001), knowledge inference through GO is widely applied in academic and industry research.
In this webcast I aim to introduce the three main branches localization, function and process that we use in GO and demonstrate how they're immediately applicable in the IoT — after all, a cell is just a large, interconnected system. I will further discuss relationship types that we use in the annotation of biological entities, and propose a few that are more appropriate for the IoT. I will contrast this relatively simple system with other ontologies suggested for the IoT. It is not my aim to sell GO as a one-size-fits-all, but talk about how building a large ontology has taught us pragmatism that is quite remote from many purely academic ontology proposals.
Presented at the Open Data Science meetup London (January 2016). To fully leverage the potential of the Internet of Things requires the exchange of information between devices. Unfortunately, most data remains in vendor silos. This talk explains how the life sciences have tackled similar issues, and why closed, vendor-specific systems may miss out.
Data Science London - Meetup, 28/05/15Boris Adryan
Slides from my @ds_ldn talk about Ontologies in the Internet of Things. Note that this is a short version of a talk that I presented earlier this month on O'Reilly Webcasts, still viewable for a while at: http://www.oreilly.com/pub/e/3365
EclipseCon France 2015 - Science TrackBoris Adryan
Software is increasingly playing a big part in scientific research, but in most cases the growth is organic. The life time of research software is often as short as the duration of a postdoctoral contract: Once the researcher moves on, custom-written niche code is frequently not well documented, components are not reusable, and the overall development effort is likely lost.
This is a case study in looking at the evolution of software for research in the field of genomics within my research group at the Department of Genetics at Cambridge University. While our research questions changed over the past decade, we moved from Perl code and regular expressions to R and statistical analysis, and from there to agent-based simulations in Java. Not only will I discuss the languages and tools used as well as the processes and how they have evolved over the years. It also covers the factors that influence the nature of the growth, such as funding, but also how 'open source' as a default has changed our development work. We also take a look into the future to see how we predict the software usage will grow.
Also, in presenting the problems and discussing possible solution, this talk will look at the role institutions play in helping address these issues. In particular the Software Sustainability Institute (SSI, http://software.ac.uk/) works in the UK to promote the development, maintenance and (re)use of research software.
The Eclipse Foundation, with the Science Working Group, works to facilitate software sharing and reuse. How can organisations like the SSI and Eclipse align their strategies and activities for maximum effect?
Industry of Things World - Berlin 19-09-16Boris Adryan
This talk makes the case for a measured use of big data pipelines and analytics methods based on the specific business case: one size doesn't fit all. Rather than buying the fastest stack and the most hyped methods, practitioners interested in analytics for Internet-of-Things deployments can save a lot of money by asking themselves a few questions that I lay out in the talk.
My talk at Smart IoT London. About adding 'context' for data analytics in the consumer IoT, touching on machine learning, hidden variables, and UX/UI of communicating probabilities.
My keynote from the Location Intelligence session at Geo-IoT World in Brussels in May 2016. How location is one of many important context variables in the interpretation of sensor data.
O'Reilly Webcast: Organizing the Internet of Things - Actionable Insight Thro...Boris Adryan
Traditional machine-to-machine (M2M) uses the internet to replace what was previously achieved through a wire. The challenges for IT are not much different to any other implementation of a prescribed business model.
But how are we going to leverage the connectedness of devices in the consumer Internet of Things (IoT) in a world in which every individual may show a different degree of technology adoption? Not everyone has the connected Crock Pot! The challenges are manifold, and while in 2015 we are still arguing about technical standards that hinder communication of things across platforms, the looming challenges of data integration are even more significant.
Even if all devices e.g. in the connected home of the future are going to speak one language, how are we generating actionable insight from the available information according to the users' need? How do we determine the appropriateness of action? An empty fridge might be alarming, but should we inform the user of an impending hunger crisis if the door hasn't been opened in a week, the heating system is set to low, the car is parked at the local airport? Draw your conclusions!
Ontologies organize things and establish their relationship to each other. They can be used for knowledge inference. For example, a car is a means of transport and ultimately an indicator of absence or presence. Some scientific domains are already making extensive use of ontologies to deal with vast amounts of information. The Gene Ontology (GO) has over 40k interlinked terms that describe cell and molecular biology. For every biological entity on that scale, we can ask: Where is it? What is its function? What process is it involved with? Benefitting from substantial government funding (in the range of > $40M from the NIH since 2001), knowledge inference through GO is widely applied in academic and industry research.
In this webcast I aim to introduce the three main branches localization, function and process that we use in GO and demonstrate how they're immediately applicable in the IoT — after all, a cell is just a large, interconnected system. I will further discuss relationship types that we use in the annotation of biological entities, and propose a few that are more appropriate for the IoT. I will contrast this relatively simple system with other ontologies suggested for the IoT. It is not my aim to sell GO as a one-size-fits-all, but talk about how building a large ontology has taught us pragmatism that is quite remote from many purely academic ontology proposals.
Presented at the Open Data Science meetup London (January 2016). To fully leverage the potential of the Internet of Things requires the exchange of information between devices. Unfortunately, most data remains in vendor silos. This talk explains how the life sciences have tackled similar issues, and why closed, vendor-specific systems may miss out.
Data Science London - Meetup, 28/05/15Boris Adryan
Slides from my @ds_ldn talk about Ontologies in the Internet of Things. Note that this is a short version of a talk that I presented earlier this month on O'Reilly Webcasts, still viewable for a while at: http://www.oreilly.com/pub/e/3365
EclipseCon France 2015 - Science TrackBoris Adryan
Software is increasingly playing a big part in scientific research, but in most cases the growth is organic. The life time of research software is often as short as the duration of a postdoctoral contract: Once the researcher moves on, custom-written niche code is frequently not well documented, components are not reusable, and the overall development effort is likely lost.
This is a case study in looking at the evolution of software for research in the field of genomics within my research group at the Department of Genetics at Cambridge University. While our research questions changed over the past decade, we moved from Perl code and regular expressions to R and statistical analysis, and from there to agent-based simulations in Java. Not only will I discuss the languages and tools used as well as the processes and how they have evolved over the years. It also covers the factors that influence the nature of the growth, such as funding, but also how 'open source' as a default has changed our development work. We also take a look into the future to see how we predict the software usage will grow.
Also, in presenting the problems and discussing possible solution, this talk will look at the role institutions play in helping address these issues. In particular the Software Sustainability Institute (SSI, http://software.ac.uk/) works in the UK to promote the development, maintenance and (re)use of research software.
The Eclipse Foundation, with the Science Working Group, works to facilitate software sharing and reuse. How can organisations like the SSI and Eclipse align their strategies and activities for maximum effect?
Industry of Things World - Berlin 19-09-16Boris Adryan
This talk makes the case for a measured use of big data pipelines and analytics methods based on the specific business case: one size doesn't fit all. Rather than buying the fastest stack and the most hyped methods, practitioners interested in analytics for Internet-of-Things deployments can save a lot of money by asking themselves a few questions that I lay out in the talk.
My talk about data and information models for IoT, how ontologies can establish the relationship between IoT devices, and how Eclipse Vorto could accommodate ontological information. Briefly features Eclipse Smarthome.
Just because you can doesn't mean that you should - thingmonk 2016Boris Adryan
Big data! Fast data! Real-time analytics! These are buzzwords commonly associated with platform offerings around IoT.
Although the Law of large numbers always applies, just because you can deploy more sensors doesn't automatically mean that you should. After all, they cost money, bandwidth, and can be a pain to maintain. On the example of the Westminster Parking Trial, I'd like to show how analytics on preliminary survey data could have reduced the number of deployed sensors significantly.
A similar logic goes for fast and real-time analytics. While being advertised as killer features, many people new to IoT and analytics are not even aware that they might get away with batch processing. On the example of flying a drone, I'd like to discuss for which use cases I'd apply edge processing (on the drone), stream or micro-batch analytics (when data arrives at the platform) or work on batched data (stored in a database).
Plattformen für das Internet der Dinge, solutions.hamburg, 05.09.16Boris Adryan
Talk in German. Abstract: Prospective end users new to IoT are overwhelmed with the vast number of offerings around IoT data brokerage, storage and analysis. This talk exemplifies some of the challenges that have to be met in real-world deployments, and why there is no one-size-fits-all IoT solution. We conclude that IoT solution providers in many cases need to consider PaaS solutions with customer-specific modifications.
Mehr und schneller ist nicht automatisch besser - data2day, 06.10.16Boris Adryan
Das Gesetz der großen Zahlen gilt immer: Die statistische Sicherheit nimmt mit der Anzahl der Datenpunkte immer zu, sofern die Datennahme fair erfolgt. Leider kostet das Sammeln der Daten oftmals Geld, und so ist man vor allem im Bereich der Sensorik (Stichwort: Internet der Dinge) gezwungen, sinnvolle Kompromisse einzugehen. In diesem Vortrag fasse ich die Erkenntnisse eines Projekts zusammen, in dem die Datenanalytik zeigte, dass man zukünftig nur 60% der ausgebrachten Sensoren wirklich braucht. Auch muss es nicht immer Echtzeit-Analyse sein: Mit einer auf den Business-Case abgestimmten Datenstrategie lassen sich unnötige Ausgaben vermeiden.
Eclipse IoT is the M2M/IoT ecosystem provided by the Eclipse Foundation. It offers open source software solutions for end devices, gateway systems and backends. Notable Eclipse IoT projects are Kura (a turn-key ready gateway e.g. for the Raspberry Pi), Eclipse SmartHome (integral part e.g. of openHAB) or the MQTT/CoAP suits Mosquitto, Paho, Californium, Wakama and Leshan. There are also solutions for process plants and manufacturing, as well as tools for large-scale device management.
On the Dynamics of Machine Learning Algorithms and Behavioral Game TheoryRikiya Takahashi
Presentation Material used in guest lecturing at University of Tsukuba on September 17, 2016.
Target audience is part-time PhD student working at a machine learning, data mining, or agent-based simulation project.
What the IoT should learn from the life sciencesBoris Adryan
What the Internet of Things should learn from the life sciences. About the utility of open data, ontologies and public repositories as routinely used in the academic life science, but rarely in the IoT.
UX Australia 2016 - The New Paradigm of Designing with Information AutomationAdam Faulkner
Artificial intelligence is starting to come of age and with it comes more intelligent ways to serve up content but what impacts does this have on design? I will discuss how we can utilise AI to rethink content hierarchy, develop new approaches to interfaces and deliver more personalised and engaging experiences.
IoT refers to the connection of everyday objects to the Internet and to one another, with the goal being to provide users with smarter, more efficient experiences.
This presentation explores the transformative impact of machine learning on the realm of cybersecurity and highlights its potential to revolutionize threat detection, prevention, and response.
MIT Enterprise Forum of Cambridge Connected Things 2017 keynote speaker: Mac Devine, VP & CTO, Emerging Technology & Advanced Innovation, IBM Cloud Division
BDW Chicago 2016 - Alan Williamson, Chief Technology Officer, One Plus Syste...Big Data Week
The world of IoT (Internet of Things) is promising large numbers of connected devices coming online powering everything in our lives. This small trickle of a few bytes of data from each device, quickly mounts up to large amounts of data, as we start to collect from millions of online sensors.
This talk will take a look at what transformations is being made at a 20 year old IoT company, here in Chicago, from scaling out from a single database, to a fully integrated cloud infrastructure to cope with not only the volume of data being produced by our devices, but the demands from our sensory data, as we monitor when industrial trash cans become empty or full, just a variety of sensors. Discovering the logistics associated with this, as we deep dive into the world of small big data in Amazon and enterprise software.
My talk about data and information models for IoT, how ontologies can establish the relationship between IoT devices, and how Eclipse Vorto could accommodate ontological information. Briefly features Eclipse Smarthome.
Just because you can doesn't mean that you should - thingmonk 2016Boris Adryan
Big data! Fast data! Real-time analytics! These are buzzwords commonly associated with platform offerings around IoT.
Although the Law of large numbers always applies, just because you can deploy more sensors doesn't automatically mean that you should. After all, they cost money, bandwidth, and can be a pain to maintain. On the example of the Westminster Parking Trial, I'd like to show how analytics on preliminary survey data could have reduced the number of deployed sensors significantly.
A similar logic goes for fast and real-time analytics. While being advertised as killer features, many people new to IoT and analytics are not even aware that they might get away with batch processing. On the example of flying a drone, I'd like to discuss for which use cases I'd apply edge processing (on the drone), stream or micro-batch analytics (when data arrives at the platform) or work on batched data (stored in a database).
Plattformen für das Internet der Dinge, solutions.hamburg, 05.09.16Boris Adryan
Talk in German. Abstract: Prospective end users new to IoT are overwhelmed with the vast number of offerings around IoT data brokerage, storage and analysis. This talk exemplifies some of the challenges that have to be met in real-world deployments, and why there is no one-size-fits-all IoT solution. We conclude that IoT solution providers in many cases need to consider PaaS solutions with customer-specific modifications.
Mehr und schneller ist nicht automatisch besser - data2day, 06.10.16Boris Adryan
Das Gesetz der großen Zahlen gilt immer: Die statistische Sicherheit nimmt mit der Anzahl der Datenpunkte immer zu, sofern die Datennahme fair erfolgt. Leider kostet das Sammeln der Daten oftmals Geld, und so ist man vor allem im Bereich der Sensorik (Stichwort: Internet der Dinge) gezwungen, sinnvolle Kompromisse einzugehen. In diesem Vortrag fasse ich die Erkenntnisse eines Projekts zusammen, in dem die Datenanalytik zeigte, dass man zukünftig nur 60% der ausgebrachten Sensoren wirklich braucht. Auch muss es nicht immer Echtzeit-Analyse sein: Mit einer auf den Business-Case abgestimmten Datenstrategie lassen sich unnötige Ausgaben vermeiden.
Eclipse IoT is the M2M/IoT ecosystem provided by the Eclipse Foundation. It offers open source software solutions for end devices, gateway systems and backends. Notable Eclipse IoT projects are Kura (a turn-key ready gateway e.g. for the Raspberry Pi), Eclipse SmartHome (integral part e.g. of openHAB) or the MQTT/CoAP suits Mosquitto, Paho, Californium, Wakama and Leshan. There are also solutions for process plants and manufacturing, as well as tools for large-scale device management.
On the Dynamics of Machine Learning Algorithms and Behavioral Game TheoryRikiya Takahashi
Presentation Material used in guest lecturing at University of Tsukuba on September 17, 2016.
Target audience is part-time PhD student working at a machine learning, data mining, or agent-based simulation project.
What the IoT should learn from the life sciencesBoris Adryan
What the Internet of Things should learn from the life sciences. About the utility of open data, ontologies and public repositories as routinely used in the academic life science, but rarely in the IoT.
UX Australia 2016 - The New Paradigm of Designing with Information AutomationAdam Faulkner
Artificial intelligence is starting to come of age and with it comes more intelligent ways to serve up content but what impacts does this have on design? I will discuss how we can utilise AI to rethink content hierarchy, develop new approaches to interfaces and deliver more personalised and engaging experiences.
IoT refers to the connection of everyday objects to the Internet and to one another, with the goal being to provide users with smarter, more efficient experiences.
This presentation explores the transformative impact of machine learning on the realm of cybersecurity and highlights its potential to revolutionize threat detection, prevention, and response.
MIT Enterprise Forum of Cambridge Connected Things 2017 keynote speaker: Mac Devine, VP & CTO, Emerging Technology & Advanced Innovation, IBM Cloud Division
BDW Chicago 2016 - Alan Williamson, Chief Technology Officer, One Plus Syste...Big Data Week
The world of IoT (Internet of Things) is promising large numbers of connected devices coming online powering everything in our lives. This small trickle of a few bytes of data from each device, quickly mounts up to large amounts of data, as we start to collect from millions of online sensors.
This talk will take a look at what transformations is being made at a 20 year old IoT company, here in Chicago, from scaling out from a single database, to a fully integrated cloud infrastructure to cope with not only the volume of data being produced by our devices, but the demands from our sensory data, as we monitor when industrial trash cans become empty or full, just a variety of sensors. Discovering the logistics associated with this, as we deep dive into the world of small big data in Amazon and enterprise software.
“The cloud: a mysterious new technology that magically fixes all my business problems.” While that’s probably what you’ve heard over and over, we’re sure there’s still the nagging voice in your head saying, “But really…what is the cloud?” That’s a fair question, as countless definitions of cloud computing appear everyday. Let us clear up some of the confusion by revealing five mysteries of the cloud.
A presentation on AI, Artificial Intelligence.
Intro of the Author
Automation vs AI
What is AI
History& Trends
Framework of Agents
Ethics
Social Economic Implications
You might have heard or read about the Internet of Things on the net somewhere. If you’re clueless about it, the Internet of Things, or IoT for short, involves interconnecting smart gadgets together using internet connectivity with the purpose of collecting data and controlling these devices. Dharmendra Rama
PDF of presentation given by John Cain, Sheldon Monteiro, Thomas McLeish for Strata London 2013: Using big data to understand the mobile in-store shopping experience.
A brief lesson on what constitutes computational decision making, from simple regression via various classification methods to deep learning. No maths, only basic concepts to teach the lingo of machine learning to a lay audience.
Development and Deployment: The Human FactorBoris Adryan
Thingmonk 2017: End-to-end IoT solutions are often highly integrated. Even small changes to the UX of a product can have profound impact on hardware requirements, while physical constraints such as battery capacity can dictate software architecture. A holistic understanding of IoT is key to efficient implementation, the “T-shaped engineer” the star in every development team. Contrast this to intellectual silos and matrix organisation, and you may see why especially large companies fail to move quickly into IoT. Similar issues strike the application of IoT. Deploying a solution in the enterprise is just a cost factor if processes are not adjusted to leverage the connected device and its data. However, changes in process often affect companies across their entire organisational structure. This can require a change of mindsets, making the success of an IoT solution depending on the human factor.
IoT-Daten: Mehr und schneller ist nicht automatisch besser.
Über optimale Sampling-Strategien, wie man rechnen kann, ob IoT sich rechnet, und warum es nicht immer Deep Learning und Real-Time-Analytics sein muss. (Folien Deutsch/Englisch)
Node-RED and Minecraft - CamJam September 2015Boris Adryan
This workshop uses the Node-RED framework as development tool for JavaScript. Building on functionality available for generic programming challenges, we’re going to use the communication standard TCP (Transmission Control Protocol) to interact with the Minecraft API (Application Programming Interface). The material is aimed at people who have had first experience with the Minecraft API on a Raspberry Pi (say, using Python), who now want to understand what's going on behind the scenes and what TCP, API and all those other acronyms mean. It also introduces flow-based programming concepts.
A significant proportion of developments in the Internet of Things (IoT) is driven by non-technical innovators and ambitious hobbyists. Node-RED targets this audience and offers a widely used rapid prototyping platform for IoT data plumbing on the basis of JavaScript. Data platforms for the IoT provide storage facilities and value in the form of visualisation & analytics to business and end users alike. This report details how Node-RED connects to 11 different platforms and what additional services these provide.
Show drafts
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...
Thingmonk 2015
1. it’s none of
your effing
business
Computers cannot think.
Machines have to learn.
From us.
We will have to have a
conversation, James.
Why are you late, James?
James? I asked why you
are late.@BorisAdryan
2. modified, image from http://www.householdappliancesworld.com
health
management
air conditioning
smart heating
communications
security
entertainment
lighting controlweather
monitoring
room occupancy
10. blog post at https://iot.ghost.io/is-it-all-machine-learning
11. there’s no absolute
truth out there
data
✓ hard facts
✓ intuitive
probability
✓ likelihood of some hypothesis
being true given the data
12. 30 40 50 60 70
average speed at this point [MPH]
time to target
[min]
10
20
30
40
50
we have a
sense for
simple
probabilities
13. data
temperature
wind speed
wind direction
precipitation
air pressure
airport code
airline
aircraft
fully booked?
avg delays
cancellations
serve booze?
black
box
training
flights
cancelled
in the past
classifier
ranked list of
relevant
features
weight of
features
thresholds for
features
performance
metric
new data
prediction
15. good decisions
are based on
experience
machine learning
is an iterative
process
training
classifier
performance
assessment
good enough?
get on with life
moredatafortraining
data
no
yes
17. the issue with
missing data
given all relevant features,
machine learning can discover
the causality between them
18. self-learning systems
will have to seek
‘missing’ data
other than saying ‘urgent
meeting’ in the calendar,
how can the system know
it’s really urgent?
…preemptively
19. things getting
more creepy…
“Is there
something you
should tell me,
Boris?
I thought your wife
was travelling…”
…when they’re
conversational
20. life is becoming
dependent on
probabilities and
abstract quantities
@BorisAdryan
adding to our anxiety of
uncertainty,
the conversational IoT may
potentially feel repetitive,
disruptive and intrusive!