from bi to ai in ksa€¦ · ksa’s 2030 vision: ... use your bi data as a starting point for ai...

MOVING FROM BI TO AI IN THE KSA MAHER ALHOSSAINI, PHD

C2AICAS - IBM WORKSHOP ON NATIONAL AI APPLICATIONS


OUTLINE

1. What do we really mean by “moving from BI to AI”?

2. Why move from BI to AI?

3. What are BI and AI anyway?

4. So, how to move from BI to AI?

5. Candidate applications in KSA

6. Challenges


1. WHAT DO WE REALLY MEAN BY “MOVING FROM BI TO AI” ?


WHAT DO WE REALLY MEAN BY “MOVING FROM BI TO AI” ?

▸ AI in business is an augmentation to BI, not a replacement.

▸ Most businesses in KSA stop at the traditional BI-level.

▸ We would like to encourage business to adopt a version of BI that is augmented with advanced AI

BI AI


2. OK. WHY SHOULD WE MOVE FROM BI TO AI?


WHY?

▸ Economical reasons:

▸ KSA’s 2030 Vision:

▸ By 2030, almost 12% of Saudi Arabia’s GDP is expected to be from AI.

▸ World is adopting AI:

▸ By 2030, almost 26% of China’s GDP is expected to be from AI.

▸ 20%-34% is the expected annual growth in of AI contribution to the GDP in the Middle east.


WHY?

▸ Technical reasons:

▸ BI’s Dashboard can become confusing because of the large amount of information shown.

▸ Big-Data can be a problem for traditional BI.

▸ Things are changing so frequently for BI to automate because of Real-time data.

▸ Many experts are needed for decision making. Some experts’ knowledge can be encoded using AI.


3. WHAT ARE BI AND AI ANYWAY?


WHAT IS BUSINESS INTELLIGENCE (BI)?

▸ Wikipedia : “the strategies and technologies used by enterprises for the data analysis of business information. BI technologies provide historical, current and predictive views of business operations.”

▸ Usually uses the dash board.


WHAT IS BUSINESS INTELLIGENCE (BI)?

▸ Example of traditional dash board elements:

▸ Pie charts of current situation

▸ Curves representing growth rates

▸ Knobs (filters) to alter visualizations

▸ Simple regression analysis


BUT HOW DOES BI WORK?

▸ Data is collected from different sources into large databases

▸ Data is then turned into meaningful information represented visually in graphs and charts

▸ Decisions are made based on this information, minimizing guess work.

▸ Example: Google Analytics is useful for website traffic analysis.


WHAT IS ARTIFICIAL INTELLIGENCE (AI)?

▸ Wikipedia : “Artificial intelligence […] is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals.“

▸ Example Applications:

▸ Predicting if an employee will resign soon

▸ Chat bots in customer service

?

Hi


BUT HOW DOES AI WORK?

▸ Machine-Learning (ML) is what often comes to mind when we mention AI.

▸ There are 3 kinds of ML:

1. Supervised: example-based prediction

2. Unsupervised: pattern recognition

3. Reinforcement: reward-based trial and error


BUT HOW DOES AI WORK?

▸ Focus often on supervised ML.

▸ There are 2 kinds of supervised predictions:

1. Regression: find the curve that represents the data.

2. Classification: find the line that separates the 2 kinds of data.


4. HOW CAN WE MOVE FROM BI TO AI?


HOW?‣ Motivational perspective:

1. Convince self that AI is important: learn more about it!

2. Convince customers that AI is important: teach them more about it!

‣ Resource-wise perspective:

1. Find and train competent data scientists

2. Big-data platform software systems

3. Faster hardware for the big data, prediction

▸ Data perspective:

1. Use your BI data as a starting point for AI

2. Use your BI data to validate AI results


HOW?▸ Technical perspective:

1. Understand the data even more, and Identify what is important and easy to forecast / predict

2. Identify and prepare the features you need for the prediction

3. Select the prediction Model(s)

4. Evaluate the Model(s)

5. Visualize the model

6. Can you simulate? Think how

7. Integrate the results with the BI results

8. Re-evaluate and repeat


5. CANDIDATE APPLICATIONS FOR AI IN KSA


CANDIDATE APPLICATIONS FOR AI IN KSA

▸ Best location prediction

▸ Customers online behavior prediction

▸ Fraud detection

▸ Pharmaceutical drugs and Food distribution based on expiry date and demand prediction

▸ Funding for start-up company


6. CHALLENGES


CHALLENGES

▸ Technical:

▸ Like BI, Data often is not clean, sometimes impossible to clean

▸ Like BI, Data often is not public

▸ Prediction is hard. It needs time to perfect.

▸ Resources:

▸ Funding AI projects is challenging, since the return is not clear to many people

▸ Lack of skilled man power in AI

▸ Education sector is not contributing well enough to the future demand of AI


CHALLENGES▸ Awareness

▸ Lack of understanding of the benefits and meaning of AI by some customers and business owners

▸ Hard to convince people to trust AI results, since it is hard to explain the results and methods.

▸ Attitude and Ethics

▸ AI is prone to self-deception because it is more difficult than BI to validate

▸ It is tempting to use the prediction power to seek information that should not be sought.

▸ Regulation

▸ Very hard to regulate AI.

?

“ALL YOU NEED IS LOTS AND LOTS OF DATA AND LOTS OF INFORMATION ABOUT WHAT THE RIGHT ANSWER IS, AND YOU'LL BE ABLE TO TRAIN A BIG NEURAL NET TO DO WHAT YOU WANT.”

Geoffrey Hinton


THANKS.

[email protected]


▸ Sources:

▸ https://www.pwc.com/m1/en/publications/potential-impact-artificial-intelligence-middle-east.html

▸ https://callminer.com/blog/business-intelligence-works-best-practices-tips/

https://www.pwc.com/m1/en/publications/potential-impact-artificial-intelligence-middle-east.html

https://www.pwc.com/m1/en/publications/potential-impact-artificial-intelligence-middle-east.html

https://callminer.com/blog/business-intelligence-works-best-practices-tips/

https://callminer.com/blog/business-intelligence-works-best-practices-tips/

from bi to ai in ksa€¦ · ksa’s 2030 vision: ... use your bi data as a starting point for ai...

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