GiTEX Africa: Revolutionizing Industries: The Next Wave of Digital Transformation Acceleration by Generative AI

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Revolutionizing Industries:
The Next Wave of Digital Transformation
Acceleration by Generative AI
Thierry Lestable, Ph.D, Dipl-lng.
Executive Director,
Artificial Intelligence and Digital Science Research Center

2. TII – Technology Innovation Institute
Generative AI
Tip of the Iceberg…

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Beyond the Hype…
Adoption on the way…

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What LLM Can Do

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LLM Market Landscape
A limited number
of major players
as cornerstone
platforms…
Platform
… Supporting
hundreds of
startups focused
on end-user
applications
Model
SIZE
[Bn parameters]
Training
tokens [bn]
LaMDA
137
168
GPT-3
175
300
Jurrasic
178
300
Gopher
280
300
Megatron-
Turing
530
270
Text Image Audio Video Code
Chatbots Search Gaming Data ML platforms
+ others

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Generative AI Evolution
Crystal ball Roadmap

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Smart Manufacturing
Industry 5.0
XR, URLLC,
eMBB
URLLC,
5GNR
Edge AI
URLLC, 5GNR Private Network
Positioning, Edge AI
Digital Twin
Network slicing, MEC
IoT (mMTC), Blockchain
IoT, Blockchain
Cobots Mixed Reality (MR)

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Software Code Generation
Github Copilot

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Self-Healing/patching SW (1/2)
Experimental Set-up
• Processor: AMD Ryzen Threadripper
PRO 3995WX
• Cores: 16
• RAM: 256 GB
• Model: MacBook Pro (2017)
• RAM: 16 GB RAM of LPDDR3
RAM (2133 MHz)
• Processor: 2.5 GHz Intel Core i7-
7660U
Code Generation
Code Repair
Benchmarks
Generate 1000 programs with the
following prompt.
Objectives
To answer the following research
questions.
RQ1: (Code generation) Are the state-
of-the-art GPT models capable of
producing compilable, semantically
correct programs?
RQ2: (Program repair) Can external
feedback improve the bug detection
and patching ability of the GPT models?
RQ: Requirement
The Quest to the Holy Grail…

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Self-Healing/patching SW (2/2)
Original
code
Modified code
LLM
Verification
Successful
Code +
Property
violation
10 to 50 lines of
compilable C code.
99.9% compilable
codes
80% of the generated code buffer overflow and dereference failures could
be fixed in a maximum of three iterations.
RQ1
RQ2
Model Checker
80% SW Fixed
in Max 3
iterations

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Cobots (Collaborative Robots)
Industry 5.0
- Leveraging the Potential of Private Network
- Ultra-Reliable Low Latency Communication (URLLC): 0.1ms
- High bandwidth (eMBB): peak Tbps, average: Gbps / (sub)TeraHz
communications
- Density sensors: 10 Millions/km2
- Teleoperation feasible
Generative AI & LLM are going to accelerate his further

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Generative AI (1/2)
A New Era is Coming…
Besides usual supportive specialized (fine-tuning) and conversational
applications, the multimodality opens new applications for Control
Command Centers, thanks to retrieval & generation of information based
on limited sources (since mapping with other ‘sensing’ has been trained).
Furthermore, introducing new Modalities is feasible and facilitated, with
vision model still as the keystone (image-paired data).
1) Text Generation & retrieval
2) Conversation
3) Summarization
4) Multi-Lingual
Multimodal
“image-paired data is sufficient to bind together these six modalities”,
NO NEED for JOINT Embeddings: paired-data sufficient.

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Generative AI (2/2)
Multi-Modality and Embodiment as Keystone To Interface With Real-World
Promising new Generation/Direction for:
• Robot Manipulation
• Task and Motion Planning (TAMP)
Whilst still performing Visual-Language
PaLM: Pathways Language model
Long
Horizon
tasks

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Digital Twins
Building and modifying Digital Twins will accelerate
Construction sites
Digital Twins Advanced Platforms:
• Simulating complex scenarios
• Generating Synthetic Data
• Training Neural Models
• Software in the Loop (SIL): Testing & validating Models
• Hardware in the Loop (HIL)
• Monitoring & actuating Platform

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Building Information Management (BIM)

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From 5G to 6G
From Connected Things to Connected Intelligence
eMBB: enhanced Mobile Broadband
mMTC: massive Machine Type Communication
uRLLC: ultra Reliable Low Latency communication
TSN: Time Sensitive Networks
6G to address UN’s SDG
➔ Contribute to the Quality & opportunities of human Life
From Connected Intelligence
To
Collective Intelligence
Source: huawei

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LLM Telecom: Foundation Model for Wireless Communications
Telecom Foundation Model
Data Telecom Languages Multi-Modal Wireless Sensors
Radio
Signals LiDAR Radar
Camer
a
Sound
Signals
Machine
Prompts
Human
Intents
Sensor
Percepti
on
SW/HW
Codes
Input
Output
Network
Protocols
Network
Configuration
Application Self-design Network Multiple Wireless Tasks
VLC
signals
Beam Power Position
Coverage
map
Waveform Modulation
Telecom
Brain
Motion
Control
Navigatio
n
Remote
Query
Telecom for LLM LLM for Telecom
Coding
Resource
LLM
Knowled
ge

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Understanding Telecom Language Through Large Language Models
Large Language Model
Radio access network
specifications
Architecture & protocols for
mobile communication systems
QoS requirements
Security & privacy
Network management &
operation
Testing & certification
....
....
....
• A stepping-stone towards realizing intent-driven and self-evolving
wireless networks from Telecom languages: using LLM to break
down natural language intents to tasks within specific network
functions.
• Adapting pretrained generative models, including BERT, DistilBERT,
RoBERTa, GPT-2 models to Telecom language.
• The framework demonstrates the applicability of adapting pretrained
generative models into the network devices, where the distilled fine-
tuned model showed accurate classification performance with
smaller model size.
Models DistilBERT BERT RoBerta GPT-2
Accuracy (%) 84 84.3 84.5 83.5
Parameters (M) 66 117 125 124
Classifying Working Groups of 3GPP Language

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LLM Telecom: Wireless Sensing and Communication
LLM for Wireless Imaging
• Conventional wireless sensing is performed through DL, which is
performed in a supervised fashion, maps received wireless signals’
characteristics into 2D images.
• Generative visual AI models, like DALL-E, CLIP and VQGAN, and
GPT Image, target to construct high-quality images from textual
descriptions.
• Adopting visual large models at the Telecom domain → create high-
resolution 3D visualization of the wireless environment using wireless
data.
• Objective: Develop foundation models that are capable of converting
wireless signals to images [for improved contextual knowledge and
enhanced communication schemes], and vice-versa.
LLM
Image
Signal
Position
(Environment)
(Channel frequency response)
(BS – UE link)

21. Generative AI will redefine roles across the organization
Carefully consider the professional identity of your employees when making changes to role definitions

22. Generative AI Risks & Mitigation
Copyright
©
20232by
Bosttoon
Coonnsultti
i
n
ngGroouupp.
.
Allll
riighttssreservved.
Source: BCG Henderson Institute
Simple mitigation
steps possible
Mitigation is
challenging
Not Exhaustive
Severity of
business risk
Ease
of
mitigation
Lack of
truth
function
High
Low
Copyright
infringement
4
Energy use and
environmental
harm
Capability overhang
Sophisticated
phishing & fraud
Biased outputs
Leaks of
proprietary data
Shadow AI
2
3
Companies should focus on risks
with high severity of business
impact, that are easilymitigated
first
Risks that are either difficult to
mitigate, or that have low potential
business impact can be tackled
second
Risks with low business impact are
toughest to mitigate, but
nonetheless important to tackle
after low hanging challenges
are addressed
1
2
3
4
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