Virtual Reality (VR) Continuum - AMP New VenturesAMP New Ventures
If the Internet is the sharing of information, then Virtual Reality (VR) is the sharing of experiences; and if most customer experiences are digital, then Virtual Reality (VR) must be important, for it is the next frontier in digital.
VR immerses users in indistinguishably real simulated environments, while Augmented Reality (AR) blends the digital into our physical environments. In the past month, PlayStation VR was released along with Google VR, to join a global ecosystem of VR content, infrastructure and platforms startups, projected to be worth $160bn by 2020.
Given It will transform experiences across industries, including Financial Services, and the expert consensus is that mainstream adoption is ~5 years away, we recommend Financial Services companies start exploring VR/AR possibilities now.
Augment Reality is set to dominate the next age of retail through product visualization. AR gonna take a little while, because there’s some really hard technology challenges there.
But it will happen. It will happen in a big way. And we will wonder, when it does [happen], how we lived without it. Kind of how we wonder how we lived without our [smartphones] today.
Mixed Reality also referred as ‘Hybrid Reality’ is a combination of both virtual reality and physical reality which creates a completely new environment where both physical and digital objects co-exist & interact in real time.
The integration of Virtual Reality & Augmented Reality with multiple mobile, wearable, IoT & conversational platforms extends possibilities for a plethora of immersive applications for a wide range of industries.
According to Statista, the combined the market size of VR & AR would be $210bn by 2022.
Virtual Reality (VR) Continuum - AMP New VenturesAMP New Ventures
If the Internet is the sharing of information, then Virtual Reality (VR) is the sharing of experiences; and if most customer experiences are digital, then Virtual Reality (VR) must be important, for it is the next frontier in digital.
VR immerses users in indistinguishably real simulated environments, while Augmented Reality (AR) blends the digital into our physical environments. In the past month, PlayStation VR was released along with Google VR, to join a global ecosystem of VR content, infrastructure and platforms startups, projected to be worth $160bn by 2020.
Given It will transform experiences across industries, including Financial Services, and the expert consensus is that mainstream adoption is ~5 years away, we recommend Financial Services companies start exploring VR/AR possibilities now.
Augment Reality is set to dominate the next age of retail through product visualization. AR gonna take a little while, because there’s some really hard technology challenges there.
But it will happen. It will happen in a big way. And we will wonder, when it does [happen], how we lived without it. Kind of how we wonder how we lived without our [smartphones] today.
Mixed Reality also referred as ‘Hybrid Reality’ is a combination of both virtual reality and physical reality which creates a completely new environment where both physical and digital objects co-exist & interact in real time.
The integration of Virtual Reality & Augmented Reality with multiple mobile, wearable, IoT & conversational platforms extends possibilities for a plethora of immersive applications for a wide range of industries.
According to Statista, the combined the market size of VR & AR would be $210bn by 2022.
The Important Difference Between Virtual Reality, Augmented Reality and Mixed...Bernard Marr
Now that virtual reality (VR), augmented reality (AR) and mixed reality (MR) are becoming more mainstream, people outside of tech circles have more questions about how these technologies differ. Here, we outline the difference between virtual reality, augmented reality, and mixed reality.
New Technology (Augmented Reality), its feature, history, use in different fields, & scope in future.
Osama Ali Mangi presents this technology's overview to his Session & Seminars.
Augmented Reality - General conclusions and recommendationsbanholzer76
Augmented realities as an emerging technology offer new opportunities due to several
shifting external conditions or changing forms of social interaction. In order to investigate
possible impacts, trends and relationships the employed method is based on an
environmental scanning and on several semi-structured interviews, conducted with several
technology experts, researchers and futurologists. The process aims to obtain valuable
information on competitors, markets, customers and suppliers as well as on macroeconomic
factors, such as social, economic, technological and political factors.
The main research findings afforded that AR can be seen as a new communication medium
or tool in an early stage level due to several major technical and social challenges and that it
goes along with the evolving information technology. These days one of the most promising
devices for AR applications is the mobile phone, which could open the floodgates for the
mass implementation of AR. With regard to the evolving ubiquity of information and
interactivity through smart things and sensors, AR will provide customized-, perceptional-,
and location-based information. Life and task enhancing services and applications are
assigned big potential in future solutions, which could occur in many areas and industries.
Due to an economical shift towards free business models, often financed by the
advertisement industry, the future development of AR will certainly be affected.
AR is a technology that superimposes a computer-generated image on a user's view of the real world, thus providing a composite view.
Presented By,
Glory J
Recent Trends And Challenges In Augmented Realitysaurabh kapoor
Augmented Reality is a developing area in the field of virtual reality research. Similarly like Virtual Reality, Augmented Reality is becoming an emerging platform for numerous applications. The work done here reveals the current state-of-the-art in Augmented Reality. Moreover current issues, trends and challenges are analyzed here.
The Augmented Reality is the cutting-edge technology which is at its new peak after the massive success of the popular game "Pokemon Go".It is estimated to cross $100 billion investment by the year 2020.
Augmented Reality; mostly confused with virtual reality is a completely different concept and is extensively implemented in various leading companies' R&D departments to experiment with design and performance characteristics.
A bit about Augmented Reality http://k3hamilton.com/AR/
Based on a presentation given on May 27, 2010 by Karen Hamilton and Jorge Olenenwa
Website has moved to http://k3hamilton.com/AR/ due to closing of wikispaces
The artificial intelligence solutions are the greatest invention of mankind that has taken the technology to a whole new level. Artificial intelligence is used by the IT sector in their systems, software, applications, websites etc.
Check it Out – https://bit.ly/2Cgmd7p
It is a seminar presentation on a technology called Virtual reality. It key features are what is virtual reality, its history and evolution, its types, devices that are used for Virtual reality and where virtual reality is applicable.
The Important Difference Between Virtual Reality, Augmented Reality and Mixed...Bernard Marr
Now that virtual reality (VR), augmented reality (AR) and mixed reality (MR) are becoming more mainstream, people outside of tech circles have more questions about how these technologies differ. Here, we outline the difference between virtual reality, augmented reality, and mixed reality.
New Technology (Augmented Reality), its feature, history, use in different fields, & scope in future.
Osama Ali Mangi presents this technology's overview to his Session & Seminars.
Augmented Reality - General conclusions and recommendationsbanholzer76
Augmented realities as an emerging technology offer new opportunities due to several
shifting external conditions or changing forms of social interaction. In order to investigate
possible impacts, trends and relationships the employed method is based on an
environmental scanning and on several semi-structured interviews, conducted with several
technology experts, researchers and futurologists. The process aims to obtain valuable
information on competitors, markets, customers and suppliers as well as on macroeconomic
factors, such as social, economic, technological and political factors.
The main research findings afforded that AR can be seen as a new communication medium
or tool in an early stage level due to several major technical and social challenges and that it
goes along with the evolving information technology. These days one of the most promising
devices for AR applications is the mobile phone, which could open the floodgates for the
mass implementation of AR. With regard to the evolving ubiquity of information and
interactivity through smart things and sensors, AR will provide customized-, perceptional-,
and location-based information. Life and task enhancing services and applications are
assigned big potential in future solutions, which could occur in many areas and industries.
Due to an economical shift towards free business models, often financed by the
advertisement industry, the future development of AR will certainly be affected.
AR is a technology that superimposes a computer-generated image on a user's view of the real world, thus providing a composite view.
Presented By,
Glory J
Recent Trends And Challenges In Augmented Realitysaurabh kapoor
Augmented Reality is a developing area in the field of virtual reality research. Similarly like Virtual Reality, Augmented Reality is becoming an emerging platform for numerous applications. The work done here reveals the current state-of-the-art in Augmented Reality. Moreover current issues, trends and challenges are analyzed here.
The Augmented Reality is the cutting-edge technology which is at its new peak after the massive success of the popular game "Pokemon Go".It is estimated to cross $100 billion investment by the year 2020.
Augmented Reality; mostly confused with virtual reality is a completely different concept and is extensively implemented in various leading companies' R&D departments to experiment with design and performance characteristics.
A bit about Augmented Reality http://k3hamilton.com/AR/
Based on a presentation given on May 27, 2010 by Karen Hamilton and Jorge Olenenwa
Website has moved to http://k3hamilton.com/AR/ due to closing of wikispaces
The artificial intelligence solutions are the greatest invention of mankind that has taken the technology to a whole new level. Artificial intelligence is used by the IT sector in their systems, software, applications, websites etc.
Check it Out – https://bit.ly/2Cgmd7p
It is a seminar presentation on a technology called Virtual reality. It key features are what is virtual reality, its history and evolution, its types, devices that are used for Virtual reality and where virtual reality is applicable.
In our latest piece, we share unique perspectives on how artificial intelligence is amplifying human potential and reshaping business. This article explore 3 fundamental questions:
How will AI shift the expectations of my customers?
How will AI transform the way my competitors run their businesses?
How should my company respond to AI?
Artificial Intelligence: investment trends and applications, H1 2016Russia.AI
The presentation explores the current state of investing in AI, including its industrial split, and provides a detailed outlook on AI applications in Healthcare, Transportation and Industrial sectors.
The PRD describes the product your company will build. The purpose is to clearly and unambiguously articulate the product’s purpose, features, functionality, and behavior.
An overview of the VR industry. In this presentation we discuss:
1. Adoption of VR technology amongst U.S. consumers
2. VR development tools and their costs, advantages, and disadvantages.
3. How developers can properly utilize VR
AI Happy Hour - Dr. Kai-Fu Lee - The Golden age of Artificial IntelligenceRicky Wong
New breakthroughs in machine learning has created two set of opportunities in artificial intelligence.
On one hand, we're seeing a tidal wave of startups in fields such as robotics, autonomous vehicles, image recognition, and speech / NLP. Separately, we're also finding AI startups leveraging big-data and solving problems in traditional, but data rich, industries (e.g. finance, retail, medical, education, etc..) and in variety of use-cases (e.g. sales, marketing, and productivity).
At Sinovation Ventures, we see China as a great platform for Artificial Intelligence to take off. China has the unique set of conditions such as large amount of untapped data and deep talent pool of engineers and scientists. As a global investment firm, we see many opportunities for US and China to partner together in the golden age of artificial intelligence.
BootstrapLabs - Tracxn Report - artificial intelligence for the Applied Arti...BootstrapLabs
This report covers companies that provide the infrastructure for creating Artificial Intelligence. These Infrastructure companies include those working on Machine Learning, Deep Learning based platforms, libraries. Some of theses companies also provide platforms for Natural Language Processing and Visual Recognition. In the Applications section, the report covers companies leveraging AI techniques to build applications tailored for end use in Enterprise, Industry & Consumer sectors.
Over $1B has been invested in AI-Infrastructure startups since 2010 with ¬$340M being invested in 2015. Over $7.5B has been invested in AI-Applications startups since 2010 with $2.3B being invested in 2015.
JyotPrakash Gugnani, Student of sem 2 from department of journalism and mass communication, JIMS Vasant Kunj II talk about Areas of Artificial Intelligence. Have a Look!! For more updates: visit: jimssouthdelhi.com
What is Artificial Intelligence?
Where is the value potential of AI?
Major Acquisitions in AI
AI business cases
AI (& BI) Ecosystem
AI challenges
Networking/expertise
Conclusion
Principles of Artificial Intelligence & Machine LearningJerry Lu
Artificial intelligence has captivated me since I worked on projects at Google that ranged from detecting fraud on Google Cloud to predicting subscriber retention on YouTube Red. Looking to broaden my professional experience, I then entered the world of venture capital by joining Baidu Ventures as its first summer investment associate where I got to work with amazingly talented founders building AI-focused startups.
Now at the Wharton School at the University of Pennsylvania, I am looking for opportunities to meet people with interesting AI-related ideas and learn about the newest innovations within the AI ecosystem. Within the first two months of business school, I connected with Nicholas Lind, a second-year Wharton MBA student who interned at IBM Watson as a data scientist. Immediately recognizing our common passion for AI, we produced a lunch-and-learn about AI and machine learning (ML) for our fellow classmates.
Using the following deck, we sought to:
- define artificial intelligence and describe its applications in business
- decode buzzwords such as “deep learning” and “cognitive computing”
- highlight analytical techniques and best practices used in AI / ML
- ultimately, educate future AI leaders
The lunch-and-learn was well received. When it became apparent that it was the topic at hand and not so much the free pizzas that attracted the overflowing audience, I was amazed at the level of interest. It was reassuring to hear that classmates were interested in learning more about the technology and its practical applications in solving everyday business challenges. Nick and I are now laying a foundation to make these workshops an ongoing effort so that more people across the various schools of engineering, design, and Penn at large can benefit.
With its focus on quantitative rigor, Wharton already feels like a perfect fit for me. In the next two years, I look forward to engaging with like-minded people, both in and out of the classroom, sharing my knowledge about AI with my peers, and learning from them in turn. By working together to expand Penn’s reach and reputation with respect to this new frontier, I’m confident that we can all grow into next-generation leaders who help drive companies forward in an era of artificial intelligence.
I’d love to hear what you think. If you found this post or the deck useful, please recommend them to your friends and colleagues!
IoT in the combination of ML can help you automate your business and optimize the processes. Let's explore the future possibilities of combining ML with IoT.
UNCOVERING FAKE NEWS BY MEANS OF SOCIAL NETWORK ANALYSISpijans
The short access to facts on social media networks in addition to its exponential upward push also made it tough to distinguish among faux information or actual facts. The quick dissemination thru manner of sharing has more high quality its falsification exponentially. It is also essential for the credibility of social media networks to avoid the spread of fake facts. So its miles rising research task to robotically check for misstatement of information thru its source, content material, or author and save you the unauthenticated assets from spreading rumours. This paper demonstrates an synthetic intelligence primarily based completely approach for the identification of the fake statements made by way of the use of social network entities. Versions of Deep neural networks are being applied to evalues datasets and have a look at for fake information presence. The implementation setup produced most volume 99% category accuracy, even as dataset is tested for binary (real or fake) labelling with multiple epochs.
UNCOVERING FAKE NEWS BY MEANS OF SOCIAL NETWORK ANALYSISpijans
The short access to facts on social media networks in addition to its exponential upward push also made it
tough to distinguish among faux information or actual facts. The quick dissemination thru manner of sharing has more high quality its falsification exponentially. It is also essential for the credibility of social media networks to avoid the spread of fake facts. So its miles rising research task to robotically check for
misstatement of information thru its source, content material, or author and save you the unauthenticated
assets from spreading rumours. This paper demonstrates an synthetic intelligence primarily based completely approach for the identification of the fake statements made by way of the use of social network
entities. Versions of Deep neural networks are being applied to evalues datasets and have a look at for
fake information presence. The implementation setup produced most volume 99% category accuracy, even
as dataset is tested for binary (real or fake) labelling with multiple epochs.
The slide helps to get an insight on the concepts of Artificial Intelligence.
The topics covered are as follows,
* Concept of AI
* Meaning of AI
* History of AI
* Levels of AI
* Types of AI
* Applications of AI - Agriculture, Health, Business (Emerging market), Education
* AI Tools and Platforms
The advent of artificial super intelligence and its impactsFernando Alcoforado
Artificial Super Intelligence will be the first technology to potentially surpass humans in all dimensions. Until now, human beings have had a monopoly on decision-making and therefore have control over everything. With Artificial Super Intelligence, this can end. A wide range of consequences can occur, including extremely good consequences and consequences as bad as the extinction of the human species.
This second machine age has seen the rise of artificial intelligence (AI), or “intelligence” that is not the result of
human cogitation. It is now ubiquitous in many commercial products, from search engines to virtual assistants. aI is the result of exponential growth in computing power, memory capacity, cloud computing, distributed and parallel processing, open-source solutions, and global connectivity of both people
and machines. The massive amounts and the speed at which structured and unstructured (e.g., text, audio, video, sensor) data is being generated has made a necessity of speedily processing and generating meaningful, actionable insights from it.
Author Francesca Rossi EN Policy Department C Citizens.docxrock73
Author: Francesca Rossi EN
Policy Department C: Citizens' Rights and Constitutional Affairs
European Parliament
PE 571.380
Artificial Intelligence: Potential Benefits and
Ethical Considerations
KEY FINDINGS
The ability of AI systems to transform vast amounts of complex, ambiguous
information into insight has the potential to reveal long-held secrets and help solve
some of the world’s most enduring problems.
However, like all powerful technologies, great care must be taken in its development
and deployment. To reap the societal benefits of AI systems, we will first need to trust
them and make sure that they follow the same ethical principles, moral values,
professional codes, and social norms that we humans would follow in the same
scenario. Research and educational efforts, as well as carefully designed regulations,
must be put in place to achieve this goal.
International Business Machines Corporation (IBM) is actively engaged, both internally
as well as with its collaborators and competitors, in global discussions about how to
make AI ethical and as beneficial as possible for people as society.
1. WHAT IS ARTIFICIAL INTELLIGENCE?
The term “artificial intelligence” (AI) has been mentioned for the first time in 1956 by John
McCarthy during a conference where several scientists decided to meet to see if machines could
be made intelligent. Since then, AI is usually defined as the capability of a computer
program to perform tasks or reasoning processes that we usually associate to intelligence
in a human being. Often it has to do with the ability to make a good decision even when there
is uncertainty or vagueness, or too much information to handle.
As an example, playing chess well, or some complex card games, is believed to need some
form of intelligence in a human being, as well as choosing the best diagnosis in a difficult
medical case, or creating something new, such as a mathematical theorem or even some form
of art, or even driving a car in the middle of a crowded city.
It is clear that this is a strange definition, because it depends on what we consider being
intelligent in the behaviour of a human being at a certain point in time. If our belief about
human intelligence changes, and we don't believe any longer that a certain task requires
intelligence, then a computer program performing that task is no longer part of AI, it becomes
just another boring computer program.
The term “artificial intelligence” brings to mind to the notion of replacing human intelligence
with something synthetic. At IBM, we prefer the term “augmented intelligence”. This means
that we aim to build systems that enhance and scale human expertise and skills rather than
replacing them. We therefore focus on practical applications of discrete AI capabilities that
assist people in performing well-defined tasks, by exploiting a wide range of AI-based services.
We also use th ...
Similar to Artificial Intelligence (2016) - AMP New Ventures (20)
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
2. 2
Artificial Intelligence is already everywhere. It powers our
smartphones, drives our cars and sorts our newsfeeds.
Companies globally and across industries are participating in the
race for true AI, to reduce operational costs, make faster, more
accurate decisions and personalise customer experiences.
Perspective
4. 4
Artificial Intelligence
The theory and development of computer systems able to
perform tasks normally requiring human intelligence, such as
visual perception, speech recognition and decision-making
Branches Applications Why nowDefinition RIsks Startup Examples
5. 5
Recent leaps of progress in AI has triggered an explosion of startups
Source: Venture Scanner
6. 1956
John McCarthy coins
‘Artificial Intelligence’
at Dartmouth
Conference
Theory
6
1950
Alan Turing
publishes paper on
concept of machine
intelligence
1995
US Department
of Defence uses
Predator UAV in
Balkan war
1997
IBM’s Deep Blue
wins chess against
World Champion
Gary Kasparov
2011
IBM Watson
computer defeats
Jeopardy game
show champions
2011
Debut of Virtual
Assistants Apple Siri
and Microsoft
Cortana
Jan 2014
Deep Mind
Team’s algorithm
wins Atari games
May 2015
Google self-driving
cars complete 1M
miles autonomously
June 2015
Deep Mind
teaches
program how to
read
AI has materialised from a theory in 1950 to widespread technological
applications that we use today in our daily lives
March 2016
AlphaGo beats Go
Grandmaster Lee
Sedol in a 5 game
series.
Strong AI: Machine Learning
Selected Milestones of AI
PresentWeak AI: Expert
Systems
Deep Learning
9. AI listens, thinks and communicates...
9
Speech Recognition is the
process of mapping audio speech
data to textual sentences or key
phrases. As humans can speak
150 words per minute on average,
but can only type 40, speech
recognition has great potential in
computer efficiency.
As more voice usage data
becomes available, speech
recognition accuracy will get better
and better. In 2010, accuracy for
technology companies hovered
around 70, and today sits between
95 and 99.
Natural language processing
(NLP) focuses on human–
computer interaction, enabling
computers to derive meaning from
human language input; and also
generate natural language
responses. Today, machines
proficiently understand natural
language syntax but face great
challenge in interpreting sentiment
(i.e. sarcasm, excitement).
Expert Systems emulate human
expert decision-making abilities.
It allows the computer to solve
for complex problems by
reasoning about knowledge,
navigating if–then rules.
(Communicate)
(Think)
(Listen)
From the creators of Siri, Viv
enables developers to create
anything on top of its,
conversational interface, making
‘her’ smarter.
10. Sees, moves and learns...
10
Computer vision is the
ability to electronically
perceive and understand
image/video sources,
extract meaningful
information and take action.
Up until now, image
recognition has been driven
by rules-based
categorisation. Today,
machines are fed data so
they build their own vision.
Motion Planning is the process
of forming a strategy of action
sequences to achieve a desired
movement, typically for execution
by intelligent agents,
autonomous robots and
unmanned vehicles. Today, we
are at advanced levels of simple
motion planning problems, such
as ‘move from A to B, while
avoiding collision with any
obstacles.’
Machine learning is training computers with
datasets to recognise patterns, develop
algorithms and self-improve. Machine
Learning has been central to today’s
unprecedented momentum in AI, as it enables
the progress of other AI branches.
(See)
(Move)
(Think)
11. 11
Machine Learning techniques are used to create self-learning capabilities
1. Raw Data is formatted and cleaned
so scientific conclusions can be drawn
without error/skew. Accuracy and
insights increase with relevance and
amount of data.
2. Algorithms are applied for
statistical analysis. This includes
things like regression models and
decision trees. The results are
examined and algorithms are re-
iterated until a best model emerges
that produces the most useful results.
Under the hood
3. A Chosen Model is now used
to produce probability scores
(usually between 0 and 1) that can
be used to make decisions, solve
problems and trigger actions.
Source: Azure
Supervised Learning :Data is labelled and
there is a specific outcome
Unsupervised Learning : Insights are drawn
from data without a specific purpose
12. 12
The goal of AI is to create Strong and Broad platforms using Machine
Learning techniques
Strong AI
Weak AI
Executes tasks within a rules-based
programmed domain
Narrow AI
Built to perform limited,
specific tasks
Broad AI
Systems that can be
applied to many contexts
Self-improves through Machine Learning
based on raw input
Goal
Knows one thing and improves Knows many things and improves
Knows many thingsKnows one or limited things
14. Agriculture
• Drone vision to monitor crop conditions like water
stress, nutrient condition, plant population, soil moisture
content etc.
• Predicting pest and disease outbreaks using data
• Drones capable of delivering customized fertilizers and
pesticides based on the requirement of each plant
• Autonomous GPS guided harvesting systems
• Facial recognition for livestock (e.g. cows)
14
Healthcare
• Expert systems to instantly weigh factors in a patients
circumstance and shortlist possible diagnoses with
confidence ratings
• Surgery Robotics to assist in the operating theatre
• Virtual nurses and remote patient monitoring
• Data to streamline the selection process of drug
development to show investigators which developments
show the most promise
• Insight and pattern induction from huge data deposits
from connected devices
Military
• Unmanned drones providing sustained surveillance and
swift precise attacks on high-value targets
• Small robots are used for missions to counter
improvised explosive devices
• Systems for faster collection and information analysis to
improve reaction and decision-making time to
implement effective military actions
• Smart pilot helmets (e.g. F35 fighter jet helmet)
Manufacturing
• Computer vision with robotics to automate assembly
line tasks
• Computer vision and machine learning to track and
isolate physical fault causes
• Mail routing using computer vision based on human
written (and often badly) postal codes
• Data-driven rapid prototyping for 3D printing
AI is being adopted across all industries
15. 15
Customer Service Chat Bots
NLP powered chat bots used to
answer general FAQ and action
simple tasks, reducing volumes
and waiting times for customers
Predictive Credit Analysis
Machine Learning algorithms
are applied to credit scores and
other personal data to assess
risk for loan applications and
loan pools as a whole
Insurance Underwriting
Underwriting AI systems are
used to automate the
underwriting process and utilise
wider and more granular data
such as health and social media
Personal Budgeting
AI is used to recognise and
report personal spending
patterns, detailing location,
merchant and spend category.
Alerts can be pushed for
irregular fees and patterns
Algorithmic Trading
Investment managers use
trading Algorithms to
automatically place trades,
generating profits at speeds
that are humanly impossible
Fraud Detection
By analysing historical transaction
data, models can be built to detect
fraudulent patterns. These models
can then be applied to real-time
financial transactions and be given
fraud scores.
Operations and Risk Product Sales & Marketing Customer Service
Marketing
AI used to personalise offers,
A/B test advertising content,
and decide when is the
optimal time to release that
content
AI is being deployed across the Financial Services value chain
Robo-Advice
Automated financial advice
and investment portfolio
rebalancing based on risk
profile and life stage
19. 6bnNetwork
connections per
person on earth
2.5 People have
smartphones.
World population =
7bn
30bnPieces of content
shared on
Facebook every
month
19
More fuel (data)
Just as human brains require dozens of examples before it
can naturally distinguish cats and dogs, Artificial minds
require large datasets to upskill in categorisation accuracy.
Social networks, mobile phones and wearable devices,
powered by improved connectivity and cloud economics,
have created an explosion of data to feed AI engines.
90% of all the data in the world being generated in the past
2 years.
Why is AI booming now?
Avalanche of Data
Data is growing at a 40% compound rate, reaching ~45
Zettabytes (ZB*) by 2020. To put things in context, 1 ZB =
1.1 Trillion Gigabytes = 2 billion years of music .
More
Fuel
Better
Engineering
Cheaper
Material
Improved
engines
20. 20
Why is AI booming now?
Repurposing of GPUs
Better Engineering
Up until now, AI applications have needed to process large amounts of data in a sequential pattern,
limiting processing speeds.However, n 2009, Andrew Ng’s team at Stanford discovered that GPU
(Graphic Processing Units) chips, typically used for gaming, could be organised to run data processes in
parallel manner.
This is important as ‘neural networks’, the primary architecture of AI software today, require many
different processes to take place simultaneously in parallel. To recognise images for example, every pixel
must be seen in context to each other, a deeply parallel task.
More
Fuel
Better
Engineering
Cheaper
Material
Improved
engines
CPU: 1-4 Serial
Processing
Cores
GPU:100’s of
Parrallel
Processing
Cores
Serial
Parallel
21. 21
Cheaper material
Computational power has steadily become cheaper over the
past 50 years as per Moore’s law, which states that overall
processing power (number of transistors on an affordable
CPU) for computers will double every two years.
This is achieved through shrinking transistors, which in turn
makes digital devices significantly cheaper and more energy-
efficient to power AI applications.
Why is AI booming now?
Cheaper Computation Power
Computer cost/performance (1992 – 2012)
Microchip transistor sizes (2000 – 2020)
More
Fuel
Better
Engineering
Cheaper
Material
Improved
engines
22. 22
Source: CB Insights
This breakthrough in AI has attracted large amounts of investment, in
turn further accelerating growth
AI Landscape: Global Yearly Financing History
Investments in AI startups have
increased nearly 6x to ~400 in 2015,
up from ~70 in 2011
23. 23
Tech giants (Google, Facebook, Amazon, IBM) are aggressively
acquiring AI startups to capture market share
Race for AI: Most Active Acquirers in Artificial Intelligence
Google is the most active in
the space (21 companies)
followed by Facebook
(10 companies).
Source: CB Insights
By 2020, the market for machine learning applications will reach ~$40bn and 60% of those applications will run on
the platform software of 4 companies (Amazon, IBM, Google and Microsoft)
25. 25
Risks of transferring responsibility and knowledge to AI
Existential risk to humanity
Futurist and Google’s Director of Engineering Ray
Kurzweil, predicts that machines will surpass humanity
in intelligence by 2029 and become ‘Superintelligent’, a
powerful state that could be difficult to control and pose
existential threats to humanity. Other technology leaders
such as Bill Gates and Elon Musk have expressed
similar concerns. Superintelligence is ranked as the 3rd
highest existential threat to humanity, after
Bioengineered pandemics and Nuclear war.
More serious cyber attacks
AI algorithms are equally as susceptible to cyberattacks
as regular software. However, because AI algorithms
are often depended on to make high-stakes decisions,
such as driving cars and controlling robots, the impact
of successful cyberattacks on AI systems could be
much more devastating than attacks in the past.
Elon Musk, Founder of Tesla and SpaceX, tweets
concerns about AI
Hackers remotely kill Jeep on a highway
(July 2015)
26. 26
Replacing human jobs
Boston Consulting Group predicts that by 2025, up to
a quarter of jobs will be replaced by either smart
software or robots. The first jobs most likely to be
affected are industrial jobs (manufacturing, cleaning),
routine information processing tasks (bookkeepers,
travel agents) and basic customer service roles (call
centres, cashiers).
Amplification of bugs
The shift from traditional programming to machine
learning means that code is often self-produced in neural
nets, as opposed to being hand-programmed. While this
is much faster, this means the code is harder to audit,
and early-stage errors or bugs can be easily amplified if
undiscovered. Extra validation measures should be
taken with machine learning to achieve high degrees of
quality assurance.
Microsoft’s Twitterbot ‘Tay’ goes rogue with tweets
Jobs requiring empathy and intuition (e.g. psychologists, clergies)
are least likely to be threatened by technology.
Risks of transferring responsibility and knowledge to AI
28. 28
Banjo
Gods eye view
Description
Banjo delves through public social media posts and uses
algorithms to identify deviations from the normal activity at a
given location. Apart from breaking news, Banjo’s use cases
include things such as track disease outbreaks and predict
insurance claim in natural disaster events. Banjo is now used by
thousands of news outlets, insurance firms, security contractors
and more.
How it works
The company divided the globe into 35 billion football-field-
size squares and spent years determining baseline activity
levels for each portion of the virtual grid. Now, any deviation
from this baseline triggers an alert to the Banjo team.
Why it matters
During the Boston bombing on April 15, 2013, the Banjo team
were able to instantaneously look at the scene in real time and
identify people of interest just minutes after the bombing
occurred.
Inception: 2011, California (US)
Social Media activity heat map used at Banjo HQ
Banjo’s computer vision classification
Funding to Date: US $121m
29. 29
Affectiva
Emotion as a service
Description
Affectiva offers a cloud based solution that reads facial
expressions, which it calls “Emotion as a Service”. Its emotion
analytics platform ‘Affdex’ is used by one third of Fortune Global
100 companies and over 1,400 brands (Unilever, Kellogg's,
MARS etc.) to understand consumer emotional engagement,
optimise business processes and improve customer
experiences.
How it works
Affectiva has collected the world's largest repository of emotion
data – 3.2 million faces analysed from 75+ countries amounting
to more than 12 billion emotion data points.
Why it matters
Affectiva allows developers to create hyper-personalized
experiences across multiple industries. For example, in gaming,
developers can create adaptive games that change based on a
player’s mood. In healthcare, clinical researchers can develop
applications that respond to a patient’s emotional state. Video
communication platforms can even modify presentations in real-
time, based on an audience’s engagement.
Funding to Date: US $33.72m
Inception: 2009, Massachusetts (US)
Testing advertisement reception using Affectiva software
Affectiva’s facial analysis to label emotional states
30. 30
Jibo
Every family needs a Robot
Description
Jibo is the world’s first social robot for the home, at 11 inches
tall and weighing 3 pounds. It’s uniquely empathetic in the way
it takes voice commands, recognises individuals, takes
photos/videos, answers queries and more.
How it works
Jibo uses machine learning, speech and facial recognition, and
natural language processing to learn from its interactions with
people. Jibo will familiarise with individuals, recognising voice
print and appearance, and alter its behaviour accordingly.
Why it matters
Interest from larger players in the smart home and
entertainment fields has grown since Jibo's 2014 reveal. In May
2016, Jibo’s team released an SDK (software developer kit) that
allows developers to create their own skills for Jibo. Jibo is a
step ahead of Amazon’s Alexa or Apple’s Siri in that it is built to
coexist socially with humans, a step closer towards fictional
characters such as Starwars’ R2D2.
Funding
$33.72m (FTD)
Location
Massachusetts (US)
Founded
2009
Founder
Rana el Kaliouby
Inception: 2012, Massachusetts (US)
Funding to Date: US $52.3m
Jibo
31. 31
Prisma
AI with a paintbrush
Description
Prisma uses machine learning algorithms to instantly transform
smartphone into stylized artworks based on unique artistic and
graphical styles.
How it works
Styles are extracted from artworks are mashed with photo data
using neural networks on a blank canvas to produce a final new
image. This is not to be confused with ‘filters’ as used in
Instagram.
Why it matters
This counters the argument that ‘machines can never develop
creativity’, as Prisma’s art has become virally popular. The app
is now being used in ~30 countries, with 300,000 installs across
10 of those countries per day.
Inception: 2016, Moscow (Russia)
Funding to Date: US $1m - $2m
32. 32
ZestFinance
Big data credit scoring
Description
Founded by ex-CIO of Google, Douglas Merrill, ZestFinance
applies algorithms to thousands of data points to make a credit
decision within seconds. Its loan product ‘Basix’ can approve
personal loans ($3000 - $5000) in minutes.
How it works
In evaluating borrowers, ZestFinance pulls data from various
credit agencies and other sources, looking at factors such as
college attendance, online restaurant ratings, phone bills and
even the way you type online. This allows the company to re-
create the holistic view of the borrower.
Why it matters
Alternative credit scoring allows Fintechs to lend to borrowers
typically not served by banks due to a lack of credit history. For
example, ZestFinance’s ‘Basix’ lends to near-prime borrowers
who just miss the cut to borrow from banks. Secondly, the
speed of data crunching means loans can be funded to
customers within minutes, much faster than traditional bank
processes. Finally, according to ZestFinance, ‘all data is credit
data’.
Inception: 2009, California (US)
Funding to Date: US $112m
Douglas Merrill, Founder (ZestFinance)