Harnessing the Power of Generative AI
Steny Sebastian
Unleashing Creativity: The Art of the Possible with AI & ML | Igniting Visionary Leadership | Transforming Enterprises with Innovation | Championing Ethical AI
Opportunities, Responsibilities and Beyond
Our journey is all about facilitating connections – connections between colleagues, customers, and at times directly with citizens, and connections to more efficient ways of working that foster transparency and value creation.
So, Why does this moment hold such significance for enterprise artificial intelligence (AI)?
Generative AI has marked significant progress in producing diverse data types, but it's the Large Language Model (LLM) that stands as a game-changer in the AI landscape. The LLM specialises in crafting natural language, rendering it exceptionally valuable for tasks like language translation, summarisation, and even creative content generation.
The Symbiotic Union of Foundational Models and LLM
We find ourselves at the onset of what some experts describe as the fourth generation of AI, marked by the rise of foundation models (FMs). What truly excites the FM approach is how it directly addresses one of the primary barriers to AI adoption within enterprises—the accessibility of "labeled data."
While enterprises have continued to harness and deploy #machinelearning (#ML) techniques, the widespread adoption of #deeplearning (#DL), with a few exceptions, has been predominantly hindered by the scarcity of labeled data and the associated costs, time, and effort required to acquire these labels. With the advent of FMs, an exceptional opportunity emerges to unlock value from the entirety of enterprise data. This is made possible by using minimal amounts of labeled data to swiftly customise and implement AI solutions tailored for specific tasks. Foundational models serve as the bedrock upon which #generativeAI systems are constructed, concentrating on specific AI facets. These models underpin the development of advanced technologies like LLMs by capturing patterns and generating coherent content within designated domains. Through the evolution and refinement of foundational models, researchers glean insights into essential concepts, algorithms, and architectures, while also addressing content generation limitations, leading to more sophisticated models.
Here is IBM , watsonx.ai and the available LLM's
Several Large Language Models (LLMs) tailored for natural language generation are at your disposal, each with distinct strengths:
llama-2-70b-chat
Provider: Meta | Source: Hugging Face
Tags: Question answering, Summarization, Retrieval-Augmented Generation, Classification, Generation, Code generation and conversion, Extraction
flan-ul2-20b
Provider: Google | Source: Hugging Face
Tags: Question answering, Summarization, Retrieval-Augmented Generation, Classification, Generation, Extraction
gpt-neox-20b
Provider: EleutherAI | Source: Hugging Face
Tags: Summarization, Classification, Generation
mt0-xxl-13b
Provider: BigScience | Source: Hugging Face
Tags: Question answering, Summarization, Classification, Generation
Note: The models isted above are Non-IBM Product governed by a third-party license that may impose use restrictions and other obligations. By using this model you agree to these terms.
granite-13b-chat-v1
Provider: IBM | Source: IBM
Tags: Question answering, Summarization, Classification, Generation, Extraction
granite-13b-instruct-v1
Provider: IBM | Source: IBM
Tags: Question answering, Summarization, Classification, Generation, Extraction
Whats existing is that has introduced new generative AI models and capabilities in its #watsonx platform, including the Granite series models designed for summarising, analysing, and generating text. See more here https://techcrunch.com/2023/09/07/ibm-rolls-out-new-generative-ai-features-and-models/
These LLMs have revolutionised the capability of machines to generate natural language, with applications spanning chatbots, virtual assistants, and content creation.
The Power of Accessibility: GPT Models and some highlights of the early use cases and their associated outcomes when artificial intelligence (AI) is applied
Generative Pre-trained Transformers, or GPT for short, represent a family of neural network models built on the transformer architecture. They stand as a significant breakthrough in artificial intelligence (AI) and serve as the driving force behind various generative AI.
Customer service: Empower customers to find solutions with easy, compelling experiences. Outcomes: Automate answers with 95% accuracy
HR automation: Reduce manual work and automate recruiting, sourcing and nurturing job candidates. Outcomes: Reduce employee mobility processing time by 50%
App modernisation, migration: Generate code, tune code generation response in real time. Outcomes: Deliver faster development output
Threat management: Reduce incident response times from hours to minutes or seconds. Outcomes: Contain potential threats 8x faster
Marketing: Increase personalisation, and improve efficiency across the content supply chain. Outcomes: Reduce content creation costs by up to 40%
Supply chain: Automate source-to-pay processes, reduce resource needs and improve cycle times. Outcomes: Reduce cost per invoice by up to 50%
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IT automation: Identify deployment issues, avoid incidents, and optimise application demand to supply. Outcomes: Reduce mean time to repair (MTTR) by 50%+
Asset management: Optimise critical asset performance and operations while delivering sustainable outcomes; Outcomes: Reduce unplanned downtime by 43%
Content creation: Ex. Enhance digital sports viewing with auto-generated spoken AI commentary: Outcomes: Scale live viewing experiences cost-effectively
Planning and analysis: Make smarter decisions, and focus on higher-value tasks with automated workflows. Outcomes: Process planning data up to 80% faster
AIOps: Assure continuous, cost-effective performance and connectivity across applications. Outcomes: Reduce application support tickets by 70%
Knowledge worker: Enable higher value work, improve decision-making, and increase productivity. Outcomes: Reduce 90% of text reading and analysis work
Regulatory compliance: Support compliance based on requirements/risks, and proactively respond to regulatory changes. Outcomes: Reduce time spent responding to issues
Environmental intelligence: Provide intelligence to proactively plan and manage the impact of severe weather and climate. Outcomes: Increase manufacturing output by 25%
Allow me to elucidate the context of Conversational AI and Generative AI.
Conversational AI is a facet of artificial intelligence that mimics human conversation. Leveraging advanced natural language processing capabilities, it comprehends and processes human language.
IBM 's watsonx Assistant serves as an example of a Conversational AI Platform. It simplifies the creation of intelligent, unified employee support systems with a user-friendly interface, delivering personalised, seamless, self-service interactions tailored to an organisation's content, business processes, and transactions. This platform enables round-the-clock engagement with employees on their preferred communication channels. Moreover, watsonx Assistant seamlessly integrates with existing digital platforms and technology stacks, connecting corporate knowledge bases, CRMs, and other backend data sources, empowering students to access information, complete tasks independently, and interact with live agents when needed.
It's important to note that watsonx Assistant encompasses a broader spectrum than ChatGPT. However, ChatGPT has sparked heightened interest in virtual assistant technology, particularly the capabilities underpinning Generative AI. We can harness the Conversational Search, a feature that capitalises on Large Language Models (LLMs) for Retrieval-Augmented Generation (RAG). This cutting-edge technology is engineered to produce precise, conversational responses rooted in your content. It highlights the efficiency of employing generative AI alongside your knowledge base to automatically generate responses for a wide array of employee queries.
Enterprises increasingly seek Generative AI solutions while emphasising the importance of trust and transparency. ChatGPT notably piqued our interest upon its launch, introducing us to the realm of Large Language Models and their underlying Foundation Models. These Foundation Models, powered by transformer technology, possess the unique ability to comprehend unlabeled data and transform inputs into meaningful outputs. Generative AI takes a step further by leveraging Foundation Models, enabling fine-tuning for specific tasks. With a simple prompt, the model generates outputs that closely resemble human-produced content, often exceeding human capabilities. This opens up a wide array of possibilities for advanced conversational AI and a myriad of potential use cases. Furthermore, the Natural Language Understanding (NLU) engine utilises a novel foundation model based on transformer architecture, significantly enhancing its capacity to comprehend human language and classify requests with minimal training effort. The AI bots, built on watsonx Assistant, exhibit exceptional classification accuracy with an average of merely 5 training examples per topic. This streamlines workflows, eliminating the need for extensive data analysis and saving valuable time.
Let me elaborate on how you can transform Employee Care: In today's fast-paced corporate world, employees demand efficient and responsive support when facing HR and IT-related challenges. However, reality often falls short of these expectations, leading to frustration and lost productivity. IBM #watsonx Assistant for #Employee Self-Service is poised to change that narrative, offering a cutting-edge solution that streamlines and elevates employee care.
The Challenge: Delayed Responses and Disjointed Support
Did you know that 69% of internal employee tickets could be resolved with just one touch? Yet, astonishingly, it takes companies more than a day to respond to them. This lag in response time hampers productivity and incurs unnecessary costs. Traditionally, employees navigate a complex landscape of siloed support solutions, each designed to address specific issues. This disjointed approach often disrupts their workflow and makes obtaining the information they need promptly challenging.
The Solution: #watsonx Assistant. #IBM #watsonx Assistant steps in as a game-changer, leveraging the power of conversational AI to enhance the employee experience. It's not just a chatbot; it's a sophisticated virtual assistant that streamlines employee interactions with HR and IT support.
Key Benefits:
In summary, autolearning AI technology enhances the user experience by offering various resolution methods. Self-learning generative AI chatbots, integrated into our Conversational AI platform, employ algorithms that autonomously learn from past interactions, continuously improving their ability to provide accurate responses and enhance conversation flow routing.
Moreover, ongoing enhancements are focused on elevating the contextual understanding of these models, thereby enabling them to generate responses that are even more precise and contextually relevant. The platform further personalises user experiences, adapting seamlessly to the unique preferences, tone, and communication style of each student or researcher. This tailored approach fosters interactions that are both natural and engaging, cultivating trust and rapport.
?In addition, diligent efforts are directed towards minimising biases within these models, ensuring they adhere to stringent ethical standards and uphold responsible AI practices.
Embracing Responsibility in the Age of AI
The utilisation of foundation models entails risks, including input-related concerns like bias and data poisoning, output-related issues such as misinformation and toxic content generation, and general governance challenges like energy consumption and accountability along the AI value chain.
To address these challenges, policymakers should adopt a risk-based approach to AI governance, wherein regulatory obligations are tailored to the level of risk posed by specific AI systems. Transparency, responsibility allocation, and flexibility should be central components of regulatory frameworks. Policymakers should also differentiate between open-domain and closed-domain applications, as well as between developers and deployers along the AI value chain. In addition, they must closely monitor emerging risks and foster collaboration with stakeholders to navigate complex issues, such as intellectual property rights in the realm of generative AI.
Ultimately, safeguarding the potential benefits of foundation models while mitigating their associated risks demands a balanced and proactive approach to AI governance, ensuring that technology remains a force for good in the economy and society. Here is A Policymaker’s Guide to Foundation Models by IBM
Senior Leadership must gain clarity on where value resides and the data necessary to deliver it. Generative AI can expedite existing tasks and elevate their quality across the data value chain, spanning data engineering, governance, and analysis. Businesses play a pivotal role in guaranteeing that humans retain the requisite responsibility and control over AI, irrespective of its role as a tool, partner, or independent agent. A swift embrace of self-governance in AI, coupled with valuable lessons from past technological deployments, should guide leaders as they collaborate with policymakers to expedite responsible AI policymaking.
As we explore the potential of generative AI, we must address the ethical considerations and legal ramifications surrounding its deployment. Responsible development and utilisation of AI will be pivotal in realising the benefits of generative AI while mitigating potential risks.
As we navigate this transformative era, the possibilities for generative AI are boundless, and it is an exhilarating period to witness the rapid evolution of artificial intelligence.
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1 年Great work legend!