Ajit Jaokar, Data Science for IoT professor at Oxford University “Enterprise AI - Artificial Intelligence for the Enterprise."

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Ajit Jaokar
Enterprise AI
Artificial Intelligence for the Enterprise
https://www.meetup.com/Big-Data-Berlin/events/236608419/

Ajit Jaokar
Oxford Uni – Data Science for IoT. Rated top
influencer for DS and IoT by kdnuggets and DS
central - World Economic Forum - Spoken at MWC(5
times), CEBIT, CTIA, Web 2.0, CNN, BBC, Oxford
Uni, Uni St Gallen, European Parliament. @feynlabs
– teaching kids Computer Science. Adivsory –
Connected Liverpool

Ajit Jaokar

AI vs. Deep Learning vs. Machine Learning.
The term Artificial Intelligence (AI) implies a machine that can
Reason. A more complete list or AI characteristics
Reasoning - Knowledge representation – Planning –
Communication - Perception:
http://cdn04.androidauthority.net/wp-content/uploads/2015/07/machine-
learning-ai-artificial-intelligence-e1462471461626.jpg

Deep Learning algorithms are currently driving AI. Finally, in a
broad sense, the term Machine Learning means the application of
any algorithm that can be applied against a dataset to find a
pattern in the data. This includes algorithms like supervised,
unsupervised, segmentation, classification, or regression.

The holy grail of AI is artificial general intelligence (aka like
Terminator!)

What we see today is mostly narrow AI (ex like the NEST thermostat).
AI is evolving rapidly.

http://assets.inhabitat.com/wp-
content/blogs.dir/1/files/2013/12/Volvo-self-driving-car-
706x369.jpg

Ajit Jaokar
What problem does Deep Learning Address

Deep learning is really about automated feature engineering.
Feature engineering involves finding connections between
variables and packaging them into a new single variable
Deep Learning suits problems
where the target function is
complex and datasets are
large but with examples of
positive and negative
cases. Deep Learning
also suits problems that
involve Hierarchy and
Abstraction.
(image source:
Yoshua Bengio)

Ajit Jaokar
Twelve types of AI problems

With this background, we now discuss the twelve types of AI
problems.
1) Domain expert: Problems which involve Reasoning based on
a complex body of knowledge
This includes tasks which are based on learning a body of knowledge
like Legal, financial etc. and then formulating a process where the
machine can simulate an expert in the field
2) Domain extension: Problems which involve extending a
complex body of Knowledge
Here, the machine learns a complex body of knowledge like
information about existing medication etc. and then can suggest new
insights to the domain itself – for example new drugs to cure diseases.

3) Complex Planner: Tasks which involve Planning
Many logistics and scheduling tasks can be done by current (non AI)
algorithms. But increasingly, as the optimization becomes complex AI
could help. AI techniques help on this case because we have large and
complex datasets where human beings cannot detect patterns but a
machine can do so easily.
4) Better communicator: Tasks which involve improving
existing communication
AI and Deep Learning benefit many communication modes such as
automatic translation, intelligent agents etc
5) New Perception: Tasks which involve Perception
AI and Deep Learning enable newer forms of Perception which enables
new services such as autonomous vehicles

6) Enterprise AI: AI meets Re-engineering the corporation!
7) Enterprise AI adding unstructured data and Cognitive
capabilities to ERP and Data warehousing
For reasons listed above, unstructured data offers a huge opportunity
for Deep Learning and hence AI.
8) Problems which impact domains due to second order
consequences of AI
“The second-order consequences of machine learning will exceed
its immediate impact. “ ex insurance
9) Problems in the near future that could benefit from improved algorithms
A catch-all category for things which were not possible in the past, could be
possible in the near future due to better algorithms or better hardware. Ex
speech recognition and translation capabilities

10) Evolution of Expert systems
Expert systems have been around for a long time. Much of the vision
of Expert systems could be implemented in AI/Deep Learning
algorithms in the near future. The IBM Watson strategy leads to an
Expert system vision. Of course, the same ideas can be implemented
independently of Watson today.
11) Super Long sequence pattern recognition
I got this title from a slide from Uber’s head of Deep Learning. The application of
AI techniques to sequential pattern recognition is still an early stage domain(and
does not yet get the kind of attention as CNNs for example) – but in my view, this
will be a rapidly expanding space. LSTMs fall in this category
12) Extending Sentiment Analysis using AI
The interplay between AI and Sentiment analysis is also a new area.

Ajit Jaokar
What is Deep Learning

How to train a Big Data algorithm?
Start with the Rules and apply them to Data OR Start with the data and
find the rules from the Data
The Top-down approach involved writing enough rules for all possible
circumstances. But this approach is obviously limited by the number of
rules and by its finite rules base.
Bottom up approach: where there are no rules :
a) No models(schema),
b) Linearity(sequence) and hierarchy is not known
c) Non deterministic – output is not known
d) Problem domain is not finite
In contrast – transactional computing is straight forward
Image source: https://www.simplilearn.com

 10 million images from YouTube videos – recognise pictures of Cats
- without telling what a cat is
 Apply them to real problems” such as image recognition, search,
and natural-language understanding,

1.Natural Language Generation: Producing text from computer
data. Currently used in customer service, report generation, and
summarizing business intelligence insights.
2.Speech Recognition: Transcribe and transform human speech into
format useful for computer applications. Currently used in interactive
voice response systems and mobile applications.
3.Virtual Agents: Getting a lot of media attention Ex Amazon, Apple
etc
4.Machine Learning Platforms: Providing algorithms, APIs, development and
training toolkits, data, as well as computing power to design, train, and deploy
models into applications, processes, and other machines.
5.AI-optimized Hardware: Graphics processing units (GPU) and appliances
specifically designed and architected to efficiently run AI-oriented computational
jobs. Ex Nvidia.
6.Decision Management: Engines that insert rules and logic into AI systems and
used for initial setup/training and ongoing maintenance and tuning. Sample
vendors: Advanced Systems Concepts, Informatica, Maana, Pegasystems,
UiPath.

7.Deep Learning Platforms: A special type of machine learning
consisting of artificial neural networks with multiple abstraction
layers.
8.Biometrics: Enable more natural interactions between humans
and machines, including but not limited to image and touch
recognition, speech, and body language.
9.Robotic Process Automation: Using scripts and other methods to
automate human action to support efficient business processes.
Currently used where it’s too expensive or inefficient for humans to
execute a task or a process. Sample vendors: Advanced Systems
Concepts, Automation Anywhere, Blue Prism, UiPath, WorkFusion.
10.Text Analytics and NLP: Natural language processing (NLP) uses and
supports text analytics by facilitating the understanding of sentence structure
and meaning, sentiment, and intent through statistical and machine learning
methods. Currently used in fraud detection and security, a wide range of
automated assistants, and applications for mining unstructured data.

Ajit Jaokar
Deep Learning Libraries

Ajit Jaokar
The Enterprise AI layer

A logical concept called the AI layer for the Enterprise. We could see
such a layer as an extension to the Data Warehouse or the ERP system.
This has tangible and practical benefits for the Enterprise with a clear
business model. One simple way is to think of it as an ‘Intelligent
Data warehouse’ i.e. an extension to either the Data warehouse or the
ERP system. For instance, an organization would transcribe call centre
agents’ interactions with customers create a more intelligent workflow, bot
etc using Deep learning algorithms.

Enterprise AI layer – What it mean to the Enterprise
So, if we imagine such a conceptual AI layer for the enterprise, what does it
mean in terms of new services that can be offered? Here are some
examples
• Bots : Bots are a great example of the use of AI to automate repetitive
tasks like scheduling meetings. Bots are often the starting point of
engagement for AI especially in Retail and Financial services
• Inferring from textual/voice narrative: Security applications to
detect suspicious behaviour, Algorithms that can draw connections
between how patients describe their symptoms etc
• Detecting patterns from vast amounts of data: Using log files to
predict future failures, predicting cyberseurity attacks etc
• Creating a knowledge base from large datasets: for example an AI
program that can read all of Wikipedia or Github.
• Creating content on scale: Using Robots to replace Writers or even to
compose Pop songs
• Predicting future workflows: Using existing patterns to predict future
workflows
• Mass personalization: in advertising
• Video and image analytics: Collision Avoidance for Drones,
Autonomous vehicles, Agricultural Crop Health Analysis etc

Ajit Jaokar
How artificial Intelligence will redefine management

Practice 1: Leave Administration to AI
Ex - juggle shift schedules because of staff members’
illnesses, vacations, or sudden departures. AI will automate
many of these tasks. Including report writing. Recently, the data
analytics company Tableau announced a partnership with Narrative
Science, a Chicago-based provider of natural language generation
tools. The result of the collaboration is Narratives for Tableau, a free
Chrome extension that automatically creates written explanations for
Tableau graphics.

Practice 2: Focus on Judgment Work
Essence of human judgment — the application of experience and
expertise to critical business decisions and practices. (knowledge of
organizational history and culture, as well as empathy and
ethical reflection. Also creative thinking and experimentation,
data analysis and interpretation, and strategy development
Practice 3: Treat Intelligent Machines as “Colleagues”
Practice 4: Work Like a Designer
Manager-designers bring together diverse ideas into integrated,
workable, and appealing solutions. They embed design thinking into the
practices of their teams and organizations.

Practice 5: Develop Social Skills and Networks

https://techcrunch.com/2016/05/13/robots-wont-just-
take-jobs-theyll-create-them/amp/

http://cloudtimes.org/wp-
content/uploads/2012/03/cloud-robotics.jpg

Ajit Jaokar, Data Science for IoT professor at Oxford University “Enterprise AI - Artificial Intelligence for the Enterprise."

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Ajit Jaokar, Data Science for IoT professor at Oxford University “Enterprise AI - Artificial Intelligence for the Enterprise."