Rapid technological advances and innovations in analytics have been reshaping the business landscape , accentuating performance and enabling emergence of new business innovations and reshaping future of work . Artificial Intelligence (AI) is at the forefront and at inflexion point of mass adoption and will have profound implications for economy , business and more broadly for humanity . CXO's and strategists across enterprises will have to factor AI in their business plan with high rigor. Here's my take on top AI trends to watch out in 2018.

1. TOP AI TRENDS IN 2018
sameerdhanrajani.wordpress.com
https://www.linkedin.com/in/sameerdhanrajani/
Sameer Dhanrajani
Chief Strategy Officer
Fractal Analytics
https://www.youtube.com/watch?v=6JD6rVsBPfU
https://twitter.com/dhanrajanis

2. THE UBIQUITOUS-
TO-BE DEEP
LEARNING THEORY
DESIGN THINKING
DISRUPTING AI
ITSELF
AUTOML: MODEL
CREATION
WITHOUT
PROGRAMMING
DEEP BEHAVIOR: AI
THAT
UNDERSTANDS
YOU
DEEP
REINFORCEMENT
LEARNING:
EMPOWERING
ROBOTS
CAPSULE
NETWORKS:
EMULATING THE
BRAIN’S VISUAL
PROCESSING
STRENGTHS
ROBOTIZATION:
AUTOMATION AND
AUTONOMY
INTELLIGENT
THINGS FOSTERING
EMBEDDED
INTELLIGENCE
RISE IN NARROW AI
IMPLEMENTATION
IN STARTUPS AND
ENTERPRISES
CONVERSATIONAL
AI ENABLING
NATURAL
INTERACTION

3. THE UBIQUITOUS-TO-BE
DEEP LEARNING THEORY
Deep neural networks, which mimic the human brain, have demonstrated their
ability to “learn” from image, audio, and text data. The combination of neural
networks (enabled by the cloud), machine learning technology, and massive data
sets (the internet), has made Deep Learning one of the most exciting AI sub-
fields recently. Yet there’s still a lot we don’t yet know about deep learning,
including how neural networks learn or why they perform so well. Understanding
precisely how deep learning works will enable its greater development and use.
Following core deep learning trends will dominate in 2018:
• A new theory that applies the information bottleneck Principle to deep
learning will help us know how neural networks learn. It suggests that after
an initial fitting phase, a deep neural network will “forget” and compress
noisy data—that is, data sets containing a lot of additional meaningless
information—while still preserving information about what the data
represents.
• Given big data sets, Deep Learning algorithms are great at Deep Pattern
Recognition, and enable things like, speech recognition, image recognition,
natural language processing. Example DeepFace, (Facebook).
• Convolution Neural Networks will be the prevalent bread-and-butter model
for DL systems. RNNs and LSTMs with its recurrent configuration and
embedded memory nodes are going to be used less simply because they
would not be competitive to a CNN based solution.
REFERENCE LINKS
https://sameerdhanrajani.wordpress.com/2017/10/14/design-
thinking-behavioral-sciences-strategic-elements-to-building-a-
successful-ai-enterprise/
https://sameerdhanrajani.wordpress.com/2017/06/03/drones-
driverless-cars-ai-at-the-core/’
3

4. DESIGN THINKING
DISRUPTING AI ITSELF
Design thinking is defined as human-centric design that builds upon the deep
understanding of our users (e.g., their tendencies, propensities, inclinations) to
generate ideas, build prototypes, share what you’ve made, embrace the art of
failure (i.e., fail fast but learn faster) and eventually put your innovative solution
out into the world. Although cognitive design thinking is in its early stages in
many enterprises, the implications are evident. Eschewing versus embracing
design thinking can mean the difference between failure and success.
The major trends in Design Thinking for 2018 will be:
• Design Thinking will see divergence from More Powerful Intelligence To More
Creative Intelligence. While algorithms can automate many routine tasks, the
narrow nature of data-driven AI implies that evolving into creative intelligence
will enhance the human-AI augmented work philosophy.
• Cases of Ethical Quandries in Enterprise premise may evolve. Unintended
algorithmic bias can lead to exclusionary and even discriminatory practices.
Accordingly, across many fields, we can start thinking about how we create
more inclusive code and employ inclusive coding practices.
• As part of CXO Strategy for Cognitive Design Thinking, CIOs will introduce them
to their organizations by first determining how it can address problems that
conventional technologies alone cannot solve. They benefit from working with
business stakeholders to identify sources of value.
REFERENCE LINKS
https://sameerdhanrajani.wordpress.com/2017/1
0/14/design-thinking-behavioral-sciences-
strategic-elements-to-building-a-successful-ai-
enterprise/
4

5. AUTOML: MODEL
CREATION WITHOUT
PROGRAMMING
Called “AutoML” for “auto-machine learning,” it allows one AI to become the
architect of another, and direct its development without the need for input from
a human engineer. Automated Machine Learning (AutoML) systems started
becoming competitive with human machine learning experts. AutoML systems
had started replacing human experts for standard machine learning analyses in
2017 and will continue the trend in 2018. The governing trends in 2018 will be:
• AutoML will take over ML model-building process. Once a data set is in a
(relatively) clean format, the AutoML system will be able to design and
optimize a machine learning pipeline faster than 99% of the humans out
there.
• All the methods of AutoML are developed to Augment data scientists’ tasks,
not to replace them. Such methods can free the data scientist from nasty,
complicated tasks (like hyperparameter optimization) that can be solved
better by machines. But analysing and drawing conclusions still has to be done
by human experts
REFERENCE LINKS
https://sameerdhanrajani.wordpress.com/2016/12/31/sa
meer-dhanrajani-2017-data-science-analytics-trends/
https://www.kdnuggets.com/2017/01/current-state-
automated-machine-learning.html
5

6. RISE IN NARROW AI
IMPLEMENTATION IN
STARTUPS AND
ENTERPRISES
AI will continue to actively expanding its footprint within the Narrow AI enterprise.
Executives will try to more fully comprehend what AI is and how they can use it to
better capitalize on business opportunities by gaining insights into their data and
engaging with customers more productively, thereby honing a competitive edge.
Investment in AI Startups will also continue to climb as last year. To date, the market
contains 2,045 AI startups and more than 17,000 market followers, with more
joining by the day. Some major trends in 2018 will be:
• Need of large volumes of labeled data to train the system can be addressed by
Lean Data Learning Techniques like transferring a model trained for one task or
domain to another. Also, Augmented Data Techniques like synthesizing new data
through simulations or interpolations helps obtain more data, thereby
augmenting existing data to improve learning.
• Auditability and ‘explainability’ of AI will go mainstream. While Explainable AI
(XAI) work is still in its infancy, enterprise AI platforms like Infosys Nia have
started including auditability and basic visualization tools to take steps towards
a system that doesn’t behave like a black box.
• High AI Startup Investments will continue to be the trend, across use cases
within the industries. Last year, VCs struck 658 deals with AI companies, nearly
five times the number four years before.
REFERENCE LINKS
https://sameerdhanrajani.wordpress.com/2017/12/18/man-
machine-ai-the-future-is-here/
https://sameerdhanrajani.wordpress.com/2017/08/20/how-
startups-can-leverage-ai-to-gain-competitive-advantage/
https://www.darpa.mil/program/explainable-artificial-
intelligence 6

7. DEEP REINFORCEMENT
LEARNING: EMPOWERING
ROBOTS
DRL is type of neural network that learns by interacting with the environment
through observations, actions, and rewards. Currently, deep learning is enabling
reinforcement learning to scale to problems that were previously intractable,
such as learning to play video games directly from pixels. Deep reinforcement
learning is poised to revolutionize the field of AI and represents a step towards
building autonomous systems with a higher level understanding of the visual
world. Hottest trends in DRL for 2018 are:
• DRL is the most general purpose of all learning techniques, so it can be used in
the most business applications. It requires less data than other techniques to
train its models.
• Deep reinforcement learning (DRL) will be used heavily to learn gaming
strategies, such as Atari and Go—including the famous AlphaGo program that
beat a human champion.
• This will become the cornerstone for robotization with DRL being the
foundation of making robots learn. Deep reinforcement learning algorithms
are also applied to robotics, allowing control policies for robots to be learned
directly from camera inputs in the real world.
REFERENCE LINKS
https://sameerdhanrajani.wordpress.com/201
7/10/10/how-cxos-are-leveraging-ai-to-pivot-
business-strategy-and-operational-models/
http://usblogs.pwc.com/emerging-
technology/top-10-ai-tech-trends-for-2018/
7

8. CAPSULE NETWORKS:
EMULATING THE BRAIN’S
VISUAL PROCESSING
STRENGTHS
Capsule networks, a new type of deep neural network, process visual information
in much the same way as the brain, which means they can maintain hierarchical
relationships. This is in stark contrast to convolutional neural networks, one of
the most widely used neural networks, which fail to take into account important
spatial hierarchies between simple and complex objects, resulting in
misclassification and a high error rate. Expect to see the widespread use of
capsule networks across many problem domains and deep neural network
architectures. The Key trends of Capsule Networks in 2018 will be:
• Since Capsule Networks consider translation equivariance, they will become
key in the development of Advanced Computer Vision.
• Capsule Networks are capable of learning by only using a fraction of the data
that a CNN would use. But current implementations are much slower than
other modern deep learning models. Coming year will witness capsule
networks being trained quickly and efficiently.
REFERENCE LINKS
HTTPS://MEDIUM.COM/AI%C2%B3-THEORY-PRACTICE-
BUSINESS/UNDERSTANDING-HINTONS-CAPSULE-
NETWORKS-PART-I-INTUITION-B4B559D1159B
https://kndrck.co/posts/capsule_networks_explained/
8

9. CONVERSATIONAL AI
ENABLING NATURAL
INTERACTION
As e-commerce (and retail in general) becomes increasingly global and
competitive, business leaders understand that Conversational AI can be a valuable
tool in reconnecting with consumers. Chatbots, in the form of virtual assistants and
automated customer service reps, are becoming increasingly common across the
industry. Meaningful applications of conversational AI are already quietly up and
running, and as cost benefits continue to pile up, the trend will accelerate in 2018.
Some of the key facets of Conversational AI in 2018 are:
• Enterprise Conversational AI will see mainstream adoption as 20% of firms will
look to add voice enabled interfaces to their existing point-and-click dashboards
and systems.
• To build good Customer Experience companies are turning to artificial
intelligence. The new AI Chabot can help customers get the answers they need.
However, instead of chatting with a human, customers are communicating with
a machine that uses trends and previous knowledge to provide the right
answers.
• (Conversational) AI is becoming the face of a brand. Some of the leading digital
businesses are already securing significant advances in their use of AI for
everyday dealings with the consumer. In only a few years, it’s likely that most
interactions won’t require a keyboard. Instead, they will be based on voice,
gesture and augmented or virtual-reality interactions.
REFERENCE LINKS
https://sameerdhanrajani.wordpress.com/2016/11/04/sameer
-dhanrajani-the-ai-powered-retail-revolution/
https://sameerdhanrajani.wordpress.com/2017/05/20/cx-ai-
dca-how-ai-is-accentuating-customer-experience-leveraging-
digital-chat-agents-chatbots/ 9

10. ROBOTIZATION:
AUTOMATION AND
AUTONOMY
RPA uses traditional computing technology to drive its decisions and responses, but
it does this on a scale large and fast enough to roughly mimic the human
perspective. According to McKinsey analysts, 81% of the time that workers spend
on manual labor can be passed on to robots; automation of data processing will
save 69% of employees’ work hours, and automation of data collection.
Technologies will change the requirements for employees: to supplement robots
(rather than compete with them), people need to develop such qualities as
creativity, emotional intelligence, and cognitive flexibility. Key 2018 trends are:
• Robotization will displace industries like supply chain, manufacturing,
Healthcare & Transportation, aimed at making our lives easier. RPA is delivering
more near-term impact, but the future may be shaped by more advanced
applications of true AI
• Cloud base AI will learn from Big Data to enable human-like social robots that
can perform usefully as personal assistants EXAMPLES: Kuka Robotics Boston
Dynamics
• Robotization will further affect jobs, especially involving administrative tasks.
World Economic Forum (WEF) states that by 2020 due to the integration of new
technologies 7.1 million people will lose their jobs, mostly white-collar workers
engaged in office and administrative routines.
REFERENCE LINKS
https://sameerdhanrajani.wordpress.com/2017/10/29/how-
rise-of-exponential-technologies-ai-rpa-blockchain-
cybersecurity-will-redefine-talent-demand-supply-landscape/
https://www.solutionanalysts.com/the-most-influential-
mobility-trends-to-look-out-for-in-2018/
10

11. INTELLIGENT THINGS
FOSTERING EMBEDDED
INTELLIGENCE
Intelligent things are physical things that go beyond the execution of rigid
programming models and exploit AI to deliver advanced behaviors that interact
more naturally with their surroundings and with people. AI is delivering enhanced
capability to many existing things, such as IoT-connected consumer and industrial
systems. This phenomenon is closely aligned with the emergence of conversational
platforms, the expansion of the IoT and the trend toward digital twins. Major trends
of Intelligent things in 2018 will be:
• AI will be embedded more often into everyday things, such as appliances,
speakers and hospital equipment. In Smart Homes avenue, the launch of more
affordable devices like Google Home Mini as well as Alexa will only serve to
increase that comfort level.
• Embedded intelligence in industrial IoT and other business scenarios will rise.
For example, today's digital stethoscope can record and store heartbeat and
respiratory sounds. Collecting a massive database of such data, relating the data
to diagnostic and treatment information, and building an AI-powered doctor
assistance app would enable doctors to receive diagnostic support in real time.
• Smartphones to become more like smart home devices, in terms of centralized
voice usage instead of using individual apps. Apple’s delayed HomePod will drive
smart home device adoption even further, and into affluent households.
REFERENCE LINKS
https://sameerdhanrajani.wordpress.com/2017/03/24/the-
best-practices-for-internet-of-things-analytics/
https://sameerdhanrajani.wordpress.com/2017/04/15/a-
perspective-to-cios-on-emerging-cross-industry-digital-models-
under-industrie-4-0/ 11

12. DEEP BEHAVIOR: AI THAT
UNDERSTANDS YOU
Much of the Behavioral Science aspect of AI till now has been more about
observing the people’s behaviors and prompting meaningful actions based on
the facts of triggers, engagement, and habits. This will further grow, and also
open doors to concepts like perceptive behavioral responses, and influencing
human behavior for social goals. Key trends for 2018 will be:
• AI in Behavioral Economics will increase in relevance to Applied AI. This will
be used in the search for new “behavioral”-type variables that affect choice.
This will also scale up research in tech-human interaction, which will require
new knowledge from behavioral economics about attention and perceived
fairness, and improve ethical decision-making.
• AI bots that nudge humans for behavior change, like altering human social
behavior in groups will go mainstream. This can counter the flaw of local and
personal focus that humans sometime exhibit which prevents the realization
of solution to a social problem.
• Perception Intelligence, which can create almost real human like behavior
states in virtual objects will rise, increasing its foothold in the Gaming ,
Entertainment and Animation Industry.
REFERENCE LINKS
https://sameerdhanrajani.wordpress.com/2017/10/10
/how-cxos-are-leveraging-ai-to-pivot-business-
strategy-and-operational-models/
https://www.cs.toronto.edu/~duvenaud/courses/csc25
41/slides/gan-applications.pdf
12

13. THANK YOU
SAMEER DHANRAJANI
sameerdhanrajani.wordpress.com
DEMYSTIFYING DATA ANALYTICS,
DECISION SCIENCE & DIGITAL
https://www.youtube.com/watch?v=6JD6rVsBPfU