Embed Size (px)
Citation preview
QUALITATIVE AI
SOLUTIONS
Teresa Escrig, PhD
Founder and CEO
Qualitative Models can be a holistic game changer for the challenges facing Big Data similar to their impact in other fields of AI like Robotics and Computer Vision.
2
“Vivamus et metus.”
Quantitative vs Qualitative AI
Quantities are numbers, raw data, without semantics
associated.
Quality is a relationship between numbers, with
semantics associated.
Quantitative techniques or probabilistic methods
have two main drawbacks:
• They are a brute force method with high
computational cost.
• There is no cognition to make sense of the
numbers, therefore improvements are limited.
Whether in Robotics, cyber space, or big data we need to
use the same common sense knowledge that is used by
people in their everyday lives. Contra intuitively, common
sense knowledge is more difficult to model than expert
knowledge, which can be quite easily modeled by expert
systems.
Qualitative Models have been demonstrated to be the best
approach to model common sense knowledge by
transforming incomplete and uncertain data from the
environment into knowledge.
A significant part of common sense knowledge encodes
our experience with the physical world we live in.
Common sense is defined, both for humans and
computers, as “the common knowledge that is possessed
by every schoolchild and the methods for making obvious
inferences from this knowledge.” Common sense
reasoning relaxes the strongly mathematical formulation
of physical laws.
3
General problem that can be solved with Qualitative AI
Qualitative AI transforms data/information into knowledge by extracting
the most relevant aspects of that data for particular applications.
Qualitative AI also transforms knowledge into wisdom, by repeating the
process of extracting relevant data from knowledge.
Benefits of having several levels of abstraction:
• Reduction of the amount of data
• Provides only the Most Relevant Information to the application at
hand
• Real Time Analysis
• Improves Decision Making
• Possibility of automatizing the process
We have 20 years of experience applying Qualitative AI to real world
situations: 1) Autonomous Robot Navigation; and 2) Cognitive Vision.
Additionally we have done extensive research in: 3) Cyber Security and 4)
Big Data and have realized that all of these current challenges have a
common structure – they all deal with Big Data - that can be solved very
efficiently using Qualitative AI instead of the more commonly used
Quantitative/Probabilistic approach.
4
Cognitive Vision
(Concept proven with two PhD thesis directed and dozens of research papers with peer review)
From any image we obtain a set of qualitative descriptions with meaning of its various aspects and objects (such as shape, color, and
relationship of the objects). That provides a qualitative tagging with meaning associated to the image and reduces the amount of data to
process or transfer from thousands of pixels with no meaning to hundreds of characters with meaning. The tagging is then integrated with an
ontology to provide names to objects, as well as concepts, which might include any commercial information associated with that particular
object.
Once the product is completely developed, anybody with Google glasses, a smartphone, or tablet could access all known information about
that object by simply taking a picture of it!
Qualitative AI in real world applications
Autonomous and intelligent Robot Navigation
(USA Patent Pending)
The numerical data provided by the robot’s sensors are transformed using our technology into
qualitative values with meaning, that can then be used by the robot to interpret the environment
and make decisions at a higher level of abstraction, similar to the way people think about the
world around us. It is faster, insensitive to common sensor errors, and allows the robot to arrive
to conclusions that they would not be able to arrive at with only quantitative-probabilistic
approaches.
Qualitative AI in real world applications
Cyber Security
Challenges: To be effective, cyber security solutions need to:
• Obtain automatic knowledge from data to support automatic decision
processes and predictions
• Process large amounts of network traffic in real-time
• Include intelligence, to automatically identify known and unknown
attacks
• Provide evidence or an explanation to support decisions when
suspicious Internet activity corresponds to new unknown attacks
• Be scalable, meaning that the solution provided for a part of the
Internet system should be able to easily expand to provide a solution for
the whole system.
The promise of Qualitative AI
Solution: We use common sense, implemented with Qualitative
Models, which transform data with uncertainty and incompletion,
into knowledge. We extract relevant information from the raw
network traffic data, store it in graph databases, and visualize it with
user-friendly web formats. Then we use ontology architecture as meta-
data to help identify general patterns of attacks. Traffic flow will be
constantly compared with these models in real-time.
Big Data
Challenges:
• A large amount of the data in big data has little or no value -it
needs to be filtered and compressed by orders of magnitude
• Automatic generation of relevant metadata is necessary
• Powerful visualization that assists interpretation is required
The power of Qualitative AI
Solution:
Qualitative models have been demonstrated to provide common
sense or cognition to transform data with uncertainty and
incompletion into knowledge. Qualitative models can be
implemented for Big Data with graph databases and data
visualization technologies. Ontologies are mandatory as formal
data representation for big data. We have analyzed these
technologies and structured them in a way that can provide the
basis for an automatic, and effective big data analysis.
• Very accessible
• Creative
• Visionary
• Out of the box thinker and innovator
• Excellent communication skills with decision makers, engineers and
programmers. I speak all these different languages
• Real world experience in the industry for over 6 years
• University professor with 20 years experience (PhD. in Computer
Science) specializing in AI and Robotics
• Good educator, patient with the ability to transition within any level
of knowledge/experience
• Leader (leader of a research group for over 10 years, and CEO of
Cognitive Robots for 6 years, fostering an atmosphere of team
excellence and growth)
• Excellent integrator of different technologies to provide new
solutions
About Teresa Escrig
• I jumped out of my comfort zone and left the University to address
real problems
• Vast network of contacts in academia and industry
• Long-term relationships with European Universities and Research
Institutes
• I have the ability and contacts to develop teams of scientists from a
global community to provide fast solutions to highly difficult and
complex problems
• I am equally comfortable talking with scientists, business people, and
developers to coordinate large teams to bring “big-dreams” into
reality
Services that we offer
• Assessment of challenges of your clients and how the Qualitative
AI toolkit can provide a solution for them. A concise report will be
provided in one week after the assessment.
• Education to empower your team (with examples relevant to your
field):
o Qualitative AI
o Read and understand research articles and patents
o How to create new patents
o How to write your own articles
• Outline a solution to the challenge using the Qualitative AI toolkit
• Direction of the implementation of the solution using your own
team
• Outsourcing the implementation of the solution to us
How we work
• First meeting – 1 hour phone call – to become familiarized with your
corporate culture and our capabilities, and to discuss your company’s goals,
timelines, and challenges.
• Send written documentation including my CV and a summary of our
technology and services
• Sign NDA
• Second meeting – 1 hour phone call – to narrow the needs of the company
• Agreement on contract according to the needs of the company
• Start relationship:
o Start classes and/or…
o Evaluation of challenges
o Definition of solutions
o Start implementation at the company
o Outsource implementation
• Ongoing relationship: regular meetings depending on the chosen service
