关键字: [Amazon Web Services re:Invent 2023, Amazon Bedrock, Generative Ai, Unlock Opportunities, Improve Productivity, Enhance Customer Experience, Optimize Business Processes]
Number of words: 1500, reading time: 8 minutes
video
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Introduction
AI/ML is no longer a buzzword used only by big tech companies. All businesses can be improved with AI/ML. Despite this, many business and IT leaders are still struggling to adopt AI/ML to solve real business challenges. In this forum, learn about machine learning use cases, the AI/ML lifecycle, basic concepts of machine learning (including generative AI), and the roles different stakeholders play at different stages. Learn how to use AI/ML to overcome business challenges.
Highlights of speech
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The video lecture focuses on how companies can strategically leverage the power of artificial intelligence and machine learning to drive innovation and uncover new opportunities. As Senior Solutions Architects at Amazon Web Services, Michael Bhatti and Rana Gupta are experienced speakers who deliver engaging discussions on how to make the most of artificial intelligence/machine learning to create business value, Engage your audience.
At the beginning of the lecture, Michael acknowledged that although most people believe that artificial intelligence/machine learning is the wave of the future, many businesses still struggle to realize its full potential and value. This challenge served as the impetus for today’s talk, which aims to provide actionable insights on best practices so attendees can apply these experiences in their own organizations.
Next, he outlined the agenda of the lecture, starting with developing effective AI/ML strategies, then leveraging generative AI to drive innovation, reviewing common use cases, and finally outlining the categories of AI/ML, including traditional Machine learning, deep learning and generative artificial intelligence.
In the first part of his discussion about AI/ML strategies, Michael busted a common misconception that skills and technology are the main barriers to AI/ML adoption. He cited a study conducted by Goldman Sachs that showed business and data challenges are actually more prevalent than talent and tools.
He further explained that few companies truly understand how to apply AI/ML to different business units to create value. As a result, businesses miss many opportunities during their operations. Additionally, most companies have not yet successfully universalized access to data, leaving information siled and unable to build solutions for teams.
To overcome these barriers, leading companies adopt an outside-in strategy, starting with customer needs or problems rather than focusing on technology. By first understanding customer needs and potential AI/ML applications that align with the company's highest goals, the business case and expected investment impact can be accurately assessed.
Michael emphasizes that during the concept stage, one should avoid focusing too much on technology. The right way of thinking is to fall in love with the problem or opportunity, not the solution. While AI/ML is a valuable enabler, technology alone cannot make an impact. Companies must always put customer needs and business goals first.
Through the referral process, he recommends first identifying your target customers and their pain points. Next, determine the benefits these customers will gain from the new AI/ML solution. Finally, evaluate how the plan aligns with overall corporate goals such as increasing revenue, reducing costs, or expanding market share. Following this approach will produce solutions with real business value.
After clarifying the problem and goals, the next critical step is to identify the data to feed the AI/ML model. Mihir emphasized that even with generative AI, data remains the key differentiator in building effective solutions. The team needs to log and access the internal and external data needed to train the algorithm. He noted that data quality also poses a significant hurdle, as information is trapped in siled data areas within companies. Addressing data accessibility and governance issues is critical to powering AI/ML engines.
Through real-life examples, Mihir cited an IDC study highlighting the most common business goals that companies strive to achieve with AI/ML. These include increasing operational efficiency, improving customer experience, increasing employee productivity and accelerating innovation.
When it comes to customer experience, personalization and virtual assistants represent high-value use cases. Personalized content greatly improves user engagement. Intelligent virtual assistants that understand the entire customer service interaction reduce costs and frustration.
For employee productivity, meeting summaries and code generation save a lot of time. Summarizing eliminates the need to manually record key discussion points. Automatic code generation accelerates developers, enabling new product features to be delivered faster.
As evidence, Amazon Cloud Technology’s generative AI service called CodeWhisperer increased developer productivity in internal teams by 50-60%. This demonstrates the huge potential of AI/ML to amplify human performance.
A common need in use cases when assessing operational efficiency is to extract information from unstructured data sources such as documents. Structured data can be extracted from a variety of document types and incorporated into business workflows and process automation. This reduces manual work while improving accuracy.
Millhill provided an overview of the classification of artificial intelligence and machine learning, specifically pointing out traditional machine learning methods that rely on historical labeled data sets for prediction or classification. Deep learning, on the other hand, uses neural networks to discover patterns and abstract meaning from complex unstructured data such as images, videos, and speech.
For example, generative AI models such as GPT-3 are trained on large amounts of data, enabling them to generate content and handle more advanced tasks. Its advanced architecture has learned correlations between entire sentences and words within documents, resulting in high-quality results.
Millhill shared some tips for working with large language models, emphasizing the importance of carefully crafting cues to shape expected responses. Furthermore, providing relevant context through retrieval enhancement generation may reduce the risk of hallucinations. He also mentioned that in some cases, fine-tuning based on domain-specific data is required because the model is not pre-trained on company-specific data.
At this point, Millhill invited colleague Rana Gupta to talk about some specific examples of companies deploying generative AI in real business challenges.
Rana provided some compelling examples of applications in healthcare, showing how a platform can be used to generate patient discharge reports by aggregating clinical notes. This saves a lot of administrative work and effort. He also explains how pharmaceutical companies are using generative AI to accelerate drug discovery and evaluate the molecular structures of past successful drugs.
In financial services, Rana noted that smart chatbots can handle complete mortgage applications, interact with customers to answer questions and guide them through the process. For investment analysts, AI assistants can automate tedious and manual coding and visualization tasks, allowing experts to focus on higher-value activities.
In marketing and e-commerce, generative models can tailor promotional emails and web content to customer interests, based on past high-performance materials. Retailers are using virtual try-on technology to reduce friction in the buying process. Rana also emphasized that conversational search is also becoming popular, with AI chatbots enabling employees to query large amounts of company documents and data.
Bedrock, as a powerful generative AI service of Amazon Cloud Technology, provides users with a convenient way. According to Rana, the service enables leading base models to be easily accessed through its rides and APIs. This allows teams to experiment and build solutions without requiring AI/ML expertise.
Bedrock’s key capabilities include easy customization on its own data, automating workflows for repetitive tasks, and creating AI agents that simulate human behavior. Users can choose from top models such as GPT-3, PaLM, Anthropic’s Claude, and Amazon’s own TITAN. Rana emphasized that Bedrock agents are able to automate manual processes with minimal coding by leveraging natural language instructions.
Finally, Rana recommended that users clarify their own needs and use exclusive data to develop unique AI solutions. He emphasized that every business area can benefit from generative AI and encouraged teams to cooperate with Amazon Cloud Technology to help them successfully complete their AI/ML journey. The key is to focus on solving business challenges rather than just getting obsessed with the technology itself. Only by aligning AI/ML planning with company goals can companies drive innovation and open up new opportunities with this powerful combination of capabilities.
Here are some highlights from the speech:
AI chatbots use basic models to respond to non-topic questions in conversations, providing users with a more natural interactive experience compared to traditional rule-driven chatbots.
Leaders demonstrate how to enable open natural language communication with Jasper by building a chatbot that can answer questions about a company's financial data.
Leaders also discussed how Bridgewater Associates is leveraging automation technology to increase the efficiency and value of investment analyst tasks.
The speaker demonstrated a system called Bedrock that is able to generate engaging sneaker marketing emails without explicitly prompting all the details.
Amazon Cloud Technology leaders enthusiastically explained to the audience how to easily build an AI agent using Bedrock—just describe in plain English what it needs to accomplish.
Summarize
Speakers highlighted that despite the huge potential of artificial intelligence (AI) and machine learning (ML) technologies, few organizations have successfully aligned them with business objectives. The biggest hurdle is not skills or technical issues, but identifying applicable scenarios and making the data available. To overcome these challenges, companies should first understand customer needs, frame the problem, and identify the data required. AI/ML technologies can then be applied across the enterprise to improve customer experience through personalization and virtual assistants, improve employee productivity through meeting summaries and code generation, and extract insights from documents to optimize processes. Deep learning is particularly suitable for processing complex unstructured data like images, videos, and speech. When trained on massive data sets, generative AI can produce novel content and drive innovations such as chatbots and drug discovery. When using generative models, clear prompts should be given to shape the tone and working style. Retrieve relevant data to provide context and reduce misunderstandings. Fine-tuning the model using domain-specific data. In summary, focus on solving business problems, not just implementing technology. Speakers shared numerous examples of AI/ML applications and encouraged starting small with customer-focused pilot projects. With the right strategy, every part of the organization can realize innovation and new opportunities with AI/ML.
Original speech
Harness AI/ML to drive innovation and unlock new opportunities-CSDN博客
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