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1、Unleashing the power of Generative AIData Driven Innovationin collaboration with2Executive SummaryIntroductionGenerative AI&the Power of DataHow Generative AI WorksGenerative AI:Reshaping OrganizationsChallenges&Opportunities of Generative AIIndustry Specific Use CasesStaying Ahead in Generative AIG
2、enerative AI Ecosystem,Top&Niche ProvidersMoving ForwardContentP.03P.04P.05P.06P.07P.08P.10P.12P.13P.183Executive SummaryGenerative AI:A Universal Catalyst for Innovation Across IndustriesGenerative AIs impact extends across all industries,transcending boundaries to reshape the way businesses operat
3、e.Whether it is optimising supply chains in manufacturing,personalising customer experiences in retail or enhancing decision-making processes in healthcare,generative AIs transformative capabilities are ubiquitous.Its ability to streamline operations,automate tasks and provide data-driven insights e
4、mpowers organisations across diverse sectors,fostering innovation and efficiency.In an increasingly interconnected world,generative AI serves as a unifying force,ushering in a new era of technological advancement and innovation for all industries.Unlocking Opportunities:Generative AIs Potential for
5、Organizational GrowthGenerative AI presents organisations with abundant opportunities amidst its specific challenges.These challenges,including data harmonisation and privacy,increasing computing needs,and data residency requirements,offer a platform for growth.Organisations can use Synthetic Data S
6、olutions for privacy and insights,offer Computing Capacity as a Service(CaaS)for efficient scaling and implement Data Localisation Solutions for compliance and trust.These strategies pave the way for organisations to address the unique challenges of generative AI and fully embrace its inherent oppor
7、tunities in the evolving data landscape.Strategic Collaborations:Maximizing Generative AIs Value PropositionTo fully harness the potential of generative AI and cover the entire value chain effectively,a strategic partnership approach is imperative.Collaborating with AI model development experts prov
8、ides organisations with access to specialised knowledge,high-quality data sources and ethical expertise vital for successful model deployment.These partnerships facilitate responsible data collection and utilisation,ensuring that generative AI models not only meet statistical accuracy standards but
9、also adhere to ethical guidelines and societal concerns.This approach enables organisations to push the boundaries of AI-driven creativity and problem-solving while ensuring a comprehensive value proposition across all aspects of the generative AI value chain.4Today,everyone is eager to engage in co
10、nversations about generative AI.Questions about its potential,upcoming developments,concerns and the opportunity for profit have taken centre stage.Nonetheless,the progress of generative AI has been under scrutiny since 2020.It was not until late 2022 that generative AI made its grand entrance into
11、the mainstream spotlight,all thanks to the ground-breaking launch of ChatGPT.This remarkable chatbot,imbued with the ability to engage in strikingly human-like interactions,was the brainchild of OpenAI.ChatGPTs debut captivated the publics imagination,sparking widespread fascination.By 2025,an impre
12、ssive 30%of businesses will have effectively adopted an AI-enhanced approach to development and testing.By 2026,generative design AI is expected to revolutionise website and mobile app development,automating a staggering 60%of the design workload.By 2027,almost 15%of new apps will be entirely create
13、d by AI,without the need for human intervention.IntroductionSource:Gartner,Gartner Experts Answer the Top Generative AI Questions for Your Enterprise,20235The Power of DataData,often referred to as the lifeblood of the digital age,is the bedrock upon which generative AI thrives.It is not just any da
14、ta,but quality data,that serves as the fuel driving AI models to achieve remarkable feats.Quality data encompasses accurate,diverse and well-structured information that serves as the foundation for AI training.Without robust data,generative AI would lack the context and knowledge necessary to produc
15、e meaningful and creative outputs.The synergy between data and generative AI is profound,as the former provides the latter with the raw materials for innovation.Definition of Generative AIGenerative AI is a cutting-edge subset of artificial intelligence.Unlike traditional AI models that primarily fo
16、cus on processing and analysing existing data,generative AI possesses a unique capabilityit creates new data,content or other outputs.At its core,generative AI is an innovators dream,wielding the power to generate text,images,music and more autonomously.It operates by learning patterns and structure
17、s from vast datasets,allowing it to generate novel and contextually relevant content.This ground-breaking technology serves as a catalyst for innovation,fuelling creative endeavours across various domains.Generative AI&the Power of Data6Generative AI represents a powerful paradigm in artificial inte
18、lligence that revolves around the creation and extrapolation of data.At its core,generative AI is a system designed to generate data and unveil novel structures within existing datasets.It accomplishes this through two distinct but complementary approaches:Low-Rank Technique:A sophisticated method t
19、hat excels at identifying recurring patterns and hidden regularities within data,thereby enabling the system to extrapolate and expand upon these.Diffusion Models:These are intricately linked to the concept of generative inverse problems.These models excel at capturing the underlying processes that
20、govern data distribution,allowing the system to generate new,plausible data points while shedding light on the intricate interplay of variables.In essence,generative AI serves as a creative force in the realm of artificial intelligence,unearthing hidden insights,patterns and structures that might ot
21、herwise remain concealed within vast datasets.How Generative AI WorksSource:Munther Dahleh,MIT Professor,Keynote at the NextTech Summit,Data Markets for Generative AI,September 2023 7Generative AI is harnessing its potential to break departmental barriers and seamlessly integrate parallel systems in
22、to a unified data ecosystem,delivering game-changing benefits to an organisations success:Streamlining Back-End For Efficiency&InnovationGenerative AI has emerged as a pivotal force in the pursuit of streamlined back-end operations,and e-commerce is a very good example.Generative AI enhances supply
23、chain logistics by optimizing inventory distribution without explicitly referencing inventory management.It also assists in demand forecasting,aiding businesses in adjusting procurement strategies proactively.Additionally,this technology enables automated anomaly detection,swiftly identifying irregu
24、larities in transactions or operations.The insights gleaned from Generative AI drive operational efficiency,allowing businesses to adapt swiftly to market changes and enhance their backend processes without directly impacting the customer-facing interface.Personalizing Front-End CX Leveraging Data I
25、nsightsAt the forefront of the Generative AI revolution is the remarkable transformation in human interaction.Generative AI powers chatbots and virtual assistants,delivering instant,human-like interactions that bridge technology and personal connection.These AI-driven tools also leverage conversatio
26、nal analytics to understand customer preferences and emotions,enabling businesses to offer personalised products and services.This transformation leads to more meaningful and efficient front-end interactions,propelling businesses towards an innovative future.Enhancing Employees ProductivityGenerativ
27、e AI seamlessly integrates across both back-end and front-end operations,empowering teams with tools and insights for streamlined operations,automation,and data-driven decisionmaking.Companionscollaborative AI assistants play a crucial role in boosting employee capabilities by offering real-time ins
28、ights and suggestions,not only enhancing customer experiences but also freeing up employees to focus on strategic initiatives and creativity.Within software development,Generative AI revolutionizes productivity by generating code snippets,offering intelligent suggestions,and accelerating the coding
29、process,ultimately enhancing software quality.This bridge between back-end and front-end functions promotes innovation,job satisfaction,and business success in todays dynamic landscape.Generative AI:Reshaping Organizations8Data Harmonization&PrivacyIn the generative AI era,organisations grapple with
30、 the intrinsic complexity and value of data within interconnected ecosystems.Organisations that can successfully harmonise and leverage their data assets will be better positioned to create comprehensive generative AI propositions that span various industries and applications.By solving the data puz
31、zle,they can offer end-to-end solutions that cater to evolving market demands.However,this pursuit also raises substantial data privacy concerns.Generative AI models rely on massive personal datasets,risking exposure to unauthorised access and malicious use.Additionally,they can be exploited to crea
32、te convincing deepfakes,which pose threats to truth,reputation and security.Robust data privacy measures are paramount in this data-driven landscape.Computing Capacity The computing capacity challenge of generative AI,particularly its voracious appetite for Graphics Processing Units(GPUs),plays a pi
33、votal role in unlocking its full potential.These GPUs serve as the powerhouse behind the swift data processing and intricate neural network training vital for advanced AI applications.As generative AI continues to push the boundaries of innovation,organizations face the imperative of substantial inv
34、estment in GPU infrastructure.Successfully harnessing and scaling GPU resources will empower organisations to tackle complex AI tasks,fuel innovation and secure a competitive advantage in the dynamic landscape of generative AI.Data ResidencyData residencys significance is intricately tied to the ris
35、e of hyperscale computing,as it underscores the imperative for organisations to navigate complex regulatory landscapes and establish robust security measures when data often moves across borders for processing;this multifaceted approach is critical in preserving data within a countrys jurisdiction a
36、nd safeguarding sensitive information,both of which are pivotal for the successful implementation of generative AI initiatives.Navigating Generative AI Challenges9Opportunities in the Generative AI Market RaceChallenge:Data Harmonization&Privacy Opportunity:Leveraging Synthetic Data SolutionsSynthet
37、ic data is artificially generated data that mimics real-world data.It can be used to train machine learning models,develop new products and services,and test existing systems without exposing real-world data.This makes it a valuable tool for addressing data privacy concerns.Digital Dubai has partner
38、ed with the Faculty AI to develop the synthetic data framework and build use cases for Dubai governments.While such applications are promising,it is essential to approach synthetic data cautiously and consider it is use on a business case basis for now.Challenge:Meeting Growing Computing Capacity Ne
39、edsOpportunity:Offering Computing Capacity As A Service(CaaS)To address the growing computational demands,Computing as a Service(CaaS)offers a flexible and cost-effective solution.CaaS providers enable businesses to scale computational resources on-demand,eliminating upfront investments and infrastr
40、ucture management.This flexibility optimises resource allocation,reducing costs while ensuring businesses have the computing power for data-intensive fields.For example,Microsoft offers a wide range of GPU-powered instances,powered by Nvidias cutting-edge GPUs,while G42 offers centres on the Condor
41、Galaxy network,a new generation of AI supercomputers powered by Cerebras Wafer Scale Engine.Challenge:Insufficient Local Computing CapacityOpportunity:Implementing Data Localization SolutionsAI requires powerful computing power and data storage,challenging on-premises use and boosting the demand for
42、 advanced localised cloud infrastructure.Microsoft announced a new service allowing customers to host their computing resources locally:Azure Stack Edge Pro.It is a hybrid cloud solution that allows to run Azure services on-premises.The service is designed for those who need to run Azure services in
43、 low-latency environments or who need to comply with data residency requirements.Sources:HE Younus Al Nasser,CEO at Dubai Data&Statistic,Keynote at Dubai Assembly for Generative AI,Synthetic Data:The Next Wave of Privacy Solutions,October 2023;Microsoft,Website,What is Azure Stack Edge Pro with GPU?
44、,May 2023ManufacturingUtilising generative AI for optimising product design,boosting operational efficiency,implementing live equipment diagnostics,guaranteeing transparency in the supply chain and deploying AI-driven maintenance assistants for machinery upkeep.10Industry Specific Use CasesGenerativ
45、e AIs transformative potential extends across a multitude of industries,redefining how we approach various challenges:Financial ServicesLeveraging generative AI to oversee portfolios,enhance the value of unstructured data,foster product innovation and streamline business operations.Additionally,empl
46、oying AI for intelligent advisory services and revolutionising financial documentation processes.AutomotiveUtilising generative AI to streamline the design process,enhance autonomous vehicle development,predict maintenance needs and customise user interactions making vehicles safer,more efficient an
47、d tailored to individual preferences.EnergyEmploying generative AI for demand forecasting,predictive maintenance,grid optimisation,and efficient energy trading.It enhances renewable energy integration,environmental impact assessment and disaster response planning,while optimising supply chains and r
48、educing carbon emissions.Source:AWS,Website,Generative AI Use Cases,202311Industry Specific Use CasesIn each industry,generative AI revolutionises practices and opens new avenues for innovation and efficiency,demonstrating its broad-reaching impact:RetailEmploying generative AI to optimise planogram
49、s,enable virtual try-on experiences,fine-tune pricing strategies,enhance product descriptions and offer tailored product recommendations to customers.Travel&HospitalityLeveraging generative AI for content generation,translation services,personalised recommendations,customer services via chatbots and
50、 virtual assistants,responsive pricing strategies adjusted to demand and seasonality,and bolstered security measures to prevent fraud in bookings and reservations.Healthcare&Life ScienceUtilising generative AI for ambient digital notetaking,medical data analysis,personalised medicine,healthcare supp
51、ort and operational efficiency.It is also applied in clinical development to improve clinical trials,automate research reporting and optimise trial enrolment processes.Media&EntertainmentUsing generative AI to scale up the production of high-quality content,enrich subscriber experiences,enhance broa
52、dcast content,automate highlight generation and facilitate automatic content tagging for efficient content management.Source:AWS,Website,Generative AI Use Cases,202312The organisations poised for success in the generative AI landscape are those that can identify and collaborate with the right partne
53、rs to craft comprehensive solutions covering model creation,execution platforms,domain-specific generative AI models and associated business applications.In this dynamic field,finding the right strategic partner is key to harnessing the full potential of generative AI and delivering tailored,value-d
54、riven solutions to clients.Building vs Buying vs Partnering Three Approaches to Generative AI BuildingIt is crucial to recognise the substantial cost associated with developing advanced AI models like GPT-4.Each release requires substantial investments,in addition to the challenge of assembling a li
55、mited talent pool well-versed in generative AI technologies.Furthermore,the availability of high-quality data,a critical fuel for these models,remains a bottleneck.The risk of model collapse due to data limitations is a significant concern.Even lower-quality data sources are predicted to be depleted
56、 by 2040,underscoring the urgency of addressing these challenges to sustain the generative AI momentum.BuyingAn emerging alternative gaining traction is the acquisition of pre-trained AI models instead of embarking on in-house development.This approach can significantly reduce variable costs and cir
57、cumvent the complexities of assembling specialised talent.However,regional constraints may come into play,with models often exhibiting biases and limitations rooted in their training data,causing issues like hallucinations and fairness concerns.Ensuring ethical and safe outputs is also a challenge,a
58、s is protecting data privacy during model training and interactions.PartneringCollaborating with AI model development experts presents a highly advantageous option.This approach provides organisations with access to specialised knowledge and high-quality data sources,effectively addressing biases,et
59、hical concerns and data privacy issues.Notably,it eliminates the need for substantial in-house investments and the challenges associated with talent acquisition,ensuring a cost-effective and efficient path to harnessing advanced AI models like GPT-4,all while benefiting from the insights and experti
60、se of seasoned professionals in the field.Staying Ahead in Generative AISources:IDC,Generative AI in EMEA:Opportunities,Risks,and Futures,2023;Lake Dai,Carnegie Mellon University Professor,Keynote at the NextTech Summit,Generative AI Today:Opportunities,Challenges,Tomorrows Ethics,September 2023 13C
61、loud ProvidersThey offer generative AI platforms and embed generative AI features into their cloud services,allowing businesses to harness AI capabilities seamlessly.These partnerships provide access to AI features such as natural language processing,image recognition and predictive analytics.Genera
62、tive AI features are integrated into productivity tools like document editors and collaboration software.Additionally,they offer developer tools with AI capabilities,making it easier for software engineers to build AI-powered applications and solutions.IT Consulting/SI PartnersThey provide consultin
63、g and advisory services to help businesses identify and implement generative AI solutions.Customised solution development is another key offering,tailoring generative AI applications to meet specific business needs.Integration services are crucial for seamlessly incorporating generative AI into exis
64、ting systems.These partners also offer life cycle management services,ensuring the continuous operation and optimisation of generative AI solutions.Generative AI EcosystemTool VendorsThey provide large language models for various horizontal and industry-specific use-cases.Additionally,they offer loc
65、al models developed by regional research institutions and government bodies.These partnerships enable businesses to access a diverse set of AI tools and models to address specific needs,from language translation and sentiment analysis to industry-specific predictive models.14Generative AI Top Provid
66、ers-IBM,Amazon&GoogleIBM uses watsonx architecture generative AI model to help operationalise businesses.This generative AI platform designed by IBM is helping businesses in various sectors.It integrates emerging generative AI capabilities,driven by foundation models and conventional machine learnin
67、g,into an extensive studio that covers the entire AI development cycle.IBM has recently launched the newly Granite series models,which are part of IBMs generative AI models designed for enterprise apps.They are large language models(LLMs)similar to OpenAIs GPT-4 and ChatGPT,which possess the capabil
68、ity to summarise,analyse,and generate text.Amazon offers a variety of LLMs on an open-source basis to develop solutions thanks to its partnership with Hugging Face.The company has also announced Amazon Bedrocks availability,enabling AWS customers to build and scale generative AI apps.Additionally,Am
69、azon offers access to Amazon Titan,foundation AI models designed to generate text,enhance search and personalisation.Amazon has recently revealed plans to leverage a novel generative AI model to enhance user experiences across its Echo devices including Alexa.The model will drive more interactive en
70、counters,encompassing body language,eye contact and gestures.Google has two large language models(LLMs):PaLM-E,an embodied multimodal language model trained to address vision,language and robotics,and Bard,a pure language model,using machine learning and natural language processing techniques to gen
71、erate human-like text responses.Google has integrated these generative AI technologies into its suite of workplace apps.Google has also invested$300M in the field of generative AI,taking a stake of 10%in the AI start-up Anthropic.Founded by former OpenAI researchers,Anthropic has developed Claude,an
72、 intelligent chatbot.It is not yet published but is a potential rival of ChatGPT.Sources:Gartner,Gartner Experts Answer the Top Generative AI Questions for Your Enterprise,2023;IBM,Website,Our Next-Generation Enterprise Studio for AI Builders;TechCrunch,IBM Rolls Out New Generative AI Features and M
73、odels,September 2023;TechCrunch,Amazon launches its Bedrock Generative AI Service in General Availability,September 2023;TechCrunch,Amazon Brings Generative AI to Alexa,September 2023;Google invest$300M in AI start-up Anthropic,February 202315Generative AI Top Providers-Microsoft01 Microsoft Copilot
74、 Microsoft Copilot is an AI-powered productivity tool that integrates with Microsoft 365 applications and Windows 11 to provide real-time intelligent assistance.It leverages large language models(LLMs)and organisations data to enhance creativity,productivity and skills.Copilot works alongside variou
75、s Microsoft 365 apps,including Word,Excel,PowerPoint,Outlook,Teams and more.It offers a range of capabilities,such as writing suggesting and code completion,data analysis and exploration,summarising key points and action items,or creating presentations and emails.It aims to streamline tasks,boost cr
76、eativity and help employees work smarter.02 Computing PowerMicrosoft has made substantial investments in AI-driven computing power.In 2016,they committed$1 billion to the creation of Azure AI,a global AI supercomputing platform.In 2019,Microsoft allocated$1 billion to OpenAI,a research lab dedicated
77、 to developing safe and beneficial artificial general intelligence.In 2023,they announced a remarkable$10 billion investment in OpenAI,intensifying their partnership to expedite AI breakthroughs.Furthermore,Microsoft collaborates with GPU manufacturers like Nvidia to innovate GPU-accelerated AI hard
78、ware and software.03 Microsoft FabricMicrosoft Fabric is an all-encompassing analytics solution for enterprises that encompasses everything from data movement to data science,real-time analytics and business intelligence.It provides a comprehensive suite of services,including data lake,data engineer
79、ing and data integration,all in one place.It aims to simplify and streamline the entire data analytics lifecycle,from data ingestion and preparation to analysis,visualisation and decision-making.It provides a unified platform that brings together various Azure data services,including Azure Data Fact
80、ory,Azure Synapse Analytics,Power BI and Azure Purview,under a single interface.Sources:Microsoft,Website,Microsoft Copilot/Microsoft News Center/Microsoft Fabric Generative AI Niche Providers16NVIDIANVIDIA is a leading provider of AI hardware and software,with significant contributions to generativ
81、e AI.Their powerful GPUs,AI frameworks and research efforts have made them a key enabler of the technologys advancement.They have established strategic partnerships with several major technology companies such as Microsoft,Adobe and Google Cloud.OpenAIOpenAI is known for its development of large lan
82、guage models(LLMs),such as GPT-3,capable of generating human-quality text,and DALL-E,capable of creating realistic images from text descriptions.OpenAIs LLMs are used by a variety of organisations,including Microsoft,Google and Meta to develop their own AI products and services.TECHVIFYTECHVIFY spec
83、ialises in developing generative AI solutions for businesses across various industries.They offer a range of products and services including text generation,image generation and data augmentation.Their solutions are designed to help businesses improve their efficiency,creativity and customer engagem
84、ent.Hugging FaceHugging Face provides a popular open-source platform for developing and sharing generative AI models.Their platform,along with their active community support,has made them a go-to choice for developers and researchers working on generative AI projects.They have partnered with Microso
85、ft,AWS and NVIDIA to streamline the development and deployment of generative AI.AI21 labsAI21 labs is a startup that has developed cutting-edge language models like Jurassic-1 Jumbo,rivalling OpenAIs GPT-3 in its ability to generate human-quality text.AI21 labs partnered with Amazon for scalable gen
86、erative AI applications and collaborated with Google Cloud for model accessibility.They also secured investments from NVIDIA on top of Google.Infrastructure PlayerInfrastructure PlayerPlatform PlayerPlatform PlayerPlatform PlayerGenerative AI Niche Providers17CohereCohere provides customised Natural
87、 Language Processing(NLP)solutions for businesses,offering a conversational AI agent for quick access to company data.It can be integrated with Microsoft apps.They also partnered with Amazon Bedrock to extend their AI models availability and with Google to power Cohere NLP platform,all while securin
88、g an investment from NVIDIA.Rain.AIRain.AI is a company that is developing AI processors or“artificial brains”to make AI more affordable and ubiquitous.Rain.AI is still in the early stages of development,having gathered a team of experienced AI researchers and engineers.Their main mission is to make
89、 Al radically cheaper.Synthesis.AISynthesis.AI is a leading synthetic data innovator,producing computer-vision-driven visuals and human simulations across industries and focusing on ethical AI development.The company has joined the AWS Partner Network to leverage their solution offering.Unlearn.AIUn
90、learn.AI uses innovative advanced machine learning methods to leverage generative AI in forecasting patient outcomes,starting with the domain of clinical trials.They produce AI-generated digital twins of individual trial participants,enabling smaller and more efficient clinical trials to bring effec
91、tive medicines to patients sooner.SkymindSkymind develops AI solutions for enterprises.They offer a suite of tools and frameworks that make it easier for businesses to build and deploy generative AI apps.Their offerings include a platform to develop and deploy AI apps,and a toolkit for building gene
92、rative AI apps.Skymind partnered with NVIDIA to provide cutting-edge biometric solutions.Platform PlayerHybrid PlayerHybrid PlayerHybrid PlayerHybrid Player18Moving ForwardThe landscape of generative AI has evolved significantly over the years,reminding us that innovation is often built on the found
93、ations of the past.While the concept of generative AI is not new,recent advancements have propelled it into the forefront of technological innovation,revolutionising various fields from natural language processing to creative content generation.One crucial lesson learned from the development and dep
94、loyment of generative AI models is the paramount importance of statistical accuracy.These models are only as reliable as the data they are trained on,emphasising the need for high-quality,diverse and representative datasets to ensure meaningful and dependable outputs.Moreover,it is imperative to rec
95、ognise that correctness in language models does not equate to the correctness of the ideas they generate.The responsibility of interpreting and validating the generated content lies with humans,as these models can inadvertently perpetuate biases or produce erroneous information.Therefore,in this rap
96、idly evolving landscape,one constant remains:the indispensability of data.Data continues to be the lifeblood of discovery and innovation in the realm of generative AI.Without access to rich,well-curated datasets,the potential of these models remains untapped.To unlock the full value proposition of g
97、enerative AI and comprehensively address the entire value chain,a strategic partnership approach becomes essential.By partnering with AI model development experts,organisations can access specialised knowledge,high-quality data sources and ethical expertise necessary for successful model deployment.
98、These partnerships allow for the responsible collection and utilisation of data,ensuring that generative AI models not only meet statistical accuracy standards but also adhere to ethical guidelines and societal concerns.In doing so,we can continue to push the boundaries of what is possible in the world of AI-driven creativity and problem-solving while ensuring a comprehensive value proposition that encompasses all aspects of the generative AI value chain.Source:Munther Dahleh,MIT Professor,Keynote at the NextTech Summit,Data Markets for Generative AI,September 2023 19