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Discover the latest trends in generative AI, explore top courses for mastering the technology, and learn how it can boost productivity in various industries.

MongoDB -

Reducing Bias in Credit Scoring with Generative AI

Credit scoring plays a pivotal role in determining who gets access to credit and on what terms. Despite its importance, however, traditional credit scoring systems have long been plagued by a series of critical issues from biases and discrimination, to limited data consideration and scalability challenges. For example, a study of US loans showed that minority borrowers were charged higher interest rates (+8%) and rejected loans more often (+14%) than borrowers from more privileged groups. The rigid nature of credit systems means that they can be slow to adapt to changing economic landscapes and evolving consumer behaviors, leaving some individuals underserved and overlooked. To overcome this, banks and other lenders are looking to adopt artificial intelligence to develop increasingly sophisticated models for scoring credit risk. In this article, we'll explore the fundamentals of credit scoring, the challenges current systems present, and delve into how artificial intelligence (AI), in particular, generative AI (genAI) can be leveraged to mitigate bias and improve accuracy. From the incorporation of alternative data sources to the development of machine learning (ML) models, we'll uncover the transformative potential of AI in reshaping the future of credit scoring. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. What is credit scoring? Credit scoring is an integral aspect of the financial landscape, serving as a numerical gauge of an individual's creditworthiness. This vital metric is employed by lenders to evaluate the potential risk associated with extending credit or lending money to individuals or businesses. Traditionally, banks rely on predefined rules and statistical models often built using linear regression or logistic regression. The models are based on historical credit data, focusing on factors such as payment history, credit utilization, and length of credit history. However, assessing new credit applicants poses a challenge, leading to the need for more accurate profiling. To cater to the underserved or unserved segments traditionally discriminated against, fintechs and digital banks are increasingly incorporating information beyond traditional credit history with alternative data to create a more comprehensive view of an individual's financial behavior. Challenges with traditional credit scoring Credit scores are integral to modern life because they serve as a crucial determinant in various financial transactions, including securing loans, renting an apartment, obtaining insurance, and even sometimes in employment screenings. Because the pursuit of credit can be a labyrinthine journey, here are some of the challenges or limitations with traditional credit scoring models that often cloud the path to credit application approval. Limited credit history: Many individuals, especially those new to the credit game, encounter a significant hurdle – limited or non-existent credit history. Traditional credit scoring models heavily rely on past credit behavior, making it difficult for individuals without a robust credit history to prove their creditworthiness. Roughly 45 million Americans lack credit scores simply because those data points do not exist for them. Inconsistent income: Irregular income, typical in part-time work or freelancing, poses a challenge for traditional credit scoring models, potentially labeling individuals as higher risk and leading to application denials or restrictive credit limits. In January 2023 in the US, 10.1% of the workforce was self-employed – almost 16.2 million people – highlighting the need for different methods of credit scoring. High utilization of existing credit: Heavy reliance on existing credit is often perceived as a signal of potential financial strain, influencing credit decisions. Credit applications may face rejection or approval with less favorable terms, reflecting concerns about the applicant's ability to judiciously manage additional credit. Lack of clarity in rejection reasons: Understanding the reasons behind rejections hinders applicants from addressing the root causes – in the UK, a study between April 2022 and April 2023 showed the main reasons for rejection included “poor credit history” (38%), “couldn’t afford the repayments” (28%) and “having too much other credit (19%). Just 10% said they weren’t told why. A lack of transparency in rejection reasons leaves applicants in the dark, making it difficult for them to address the root cause and enhance their creditworthiness for future applications. Lack of flexibility: Shifts in consumer behavior, especially among younger generations preferring digital transactions, challenge traditional models. Factors like the rise of the gig economy, non-traditional employment, student loan debt, and high living costs complicate assessing income stability and financial health. Traditional credit risk predictions are limited during unprecedented disruptions like COVID-19, not taking this into account in scoring models. Recognizing these challenges highlights the need for alternative credit scoring models that can adapt to evolving financial behaviors, handle non-traditional data sources, and provide a more inclusive and accurate assessment of creditworthiness in today's dynamic financial landscape. Credit scoring with alternative data Alternative credit scoring refers to the use of non-traditional data sources (aka. alternative data) and methods to assess an individual's creditworthiness. While traditional credit scoring relies heavily on credit history from major credit bureaus, alternative credit scoring incorporates a broader range of factors to create a more comprehensive picture of a person's financial behavior. Below are some of the popular alternative data sources: Utility payments: Beyond credit history, consistent payments for utilities like electricity and water offer a powerful indicator of financial responsibility and reveal a commitment to meeting financial obligations, providing crucial insights beyond traditional metrics. Rental history: For those without a mortgage, rental payment history emerges as a key alternative data source. Demonstrating consistent and timely rent payments paints a comprehensive picture of financial discipline and reliability. Mobile phone usage patterns: The ubiquity of mobile phones unlocks a wealth of alternative data. Analyzing call and text patterns provides insights into an individual's network, stability, and social connections, contributing valuable information for credit assessments. Online shopping behavior: Examining the frequency, type, and amount spent on online purchases offers valuable insights into spending behaviors, contributing to a more nuanced understanding of financial habits. Educational and employment background: Alternative credit scoring considers an individual's educational and employment history. Positive indicators, such as educational achievements and stable employment, play a crucial role in assessing financial stability. These alternative data sources represent a shift towards a more inclusive, nuanced, and holistic approach to credit assessments. As financial technology continues to advance, leveraging these alternative data sets ensures a more comprehensive evaluation of creditworthiness, marking a transformative step in the evolution of credit scoring models. Alternative credit scoring with artificial intelligence Besides the use of alternative data, the use of AI as an alternative method has emerged as a transformative force to address the challenges of traditional credit scoring, for a number of reasons: Ability to mitigate bias: Like traditional statistical models, AI models, including LLMs, trained on historical data that are biased will inherit biases present in that data, leading to discriminatory outcomes. LLMs might focus on certain features more than others or may lack the ability to understand the broader context of an individual's financial situation leading to biased decision-making. However, there are various techniques to mitigate the bias of AI models: Mitigation strategies: Initiatives begin with the use of diverse and representative training data to avoid reinforcing existing biases. Inadequate or ineffective mitigation strategies can result in biased outcomes persisting in AI credit scoring models. Careful attention to the data collected and model development is crucial in mitigating this bias. Incorporating alternative data for credit scoring plays a critical role in reducing biases. Rigorous bias detection tools, fairness constraints, and regularization techniques during training enhance model accountability: Balancing feature representation and employing post-processing techniques and specialized algorithms contribute to bias mitigation. Inclusive model evaluation, continuous monitoring, and iterative improvement, coupled with adherence to ethical guidelines and governance practices, complete a multifaceted approach to reducing bias in AI models. This is particularly significant in addressing concerns related to demographic or socioeconomic biases that may be present in historical credit data. Regular bias audits: Conduct regular audits to identify and mitigate biases in LLMs. This may involve analyzing model outputs for disparities across demographic groups and adjusting the algorithms accordingly. Transparency and explainability: Increase transparency and explainability in LLMs to understand how decisions are made. This can help identify and address biased decision-making processes. Trade Ledger, a lending software as a service (SaaS) tool, uses a data-driven approach to make informed decisions with greater transparency and traceability by bringing data from multiple sources with different schemas into a single data source. Ability to analyze vast and diverse datasets: Unlike traditional models that rely on predefined rules and historical credit data, AI models can process a myriad of information, including non-traditional data sources, to create a more comprehensive assessment of an individual's creditworthiness, ensuring that a broader range of financial behaviors is considered. AI brings unparalleled adaptability to the table: As economic conditions change and consumer behaviors evolve, AI-powered models can quickly adjust and learn from new data. The continuous learning aspect ensures that credit scoring remains relevant and effective in the face of ever-changing financial landscapes. The most common objections from banks to not using AI in credit scoring are transparency and explainability in credit decisions. The inherent complexity of some AI models, especially deep learning algorithms, may lead to challenges in providing clear explanations for credit decisions. Fortunately, the transparency and interpretability of AI models have seen significant advancements. Techniques like SHapley Additive exPlanations (SHAP) values and Local Interpretable Model-Agnostic Explanations (LIME) plots and several other advancements in the domain of Explainable AI (XAI) now allow us to understand how the model arrives at specific credit decisions. This not only enhances trust in the credit scoring process but also addresses the common critique that AI models are "black boxes." Understanding the criticality of leveraging alternative data that often comes in a semi or unstructured format, financial institutions work with MongoDB to enhance their credit application processes with a faster, simpler, and more flexible way to make payments and offer credit: Amar Bank, Indonesia's leading digital bank, is combatting bias by providing microloans to people who wouldn’t be able to get financial services from traditional banks (unbanked and underserved). Traditional underwriting processes were inadequate for customers lacking credit history or collateral so they have streamlined lending decisions by harnessing unstructured data. Leveraging MongoDB Atlas, they developed a predictive analytics model integrating structured and unstructured data to assess borrower creditworthiness. MongoDB's scalability and capability to manage diverse data types were instrumental in expanding and optimizing their lending operations. For the vast majority of Indians, getting credit is typically challenging due to stringent regulations and a lack of credit data. Through the use of modern underwriting systems, slice, a leading innovator in India’s fintech ecosystem, is helping broaden the accessibility to credit in India by streamlining their KYC process for a smoother credit experience. By utilizing MongoDB Atlas across different use cases, including as a real-time ML feature store, slice transformed their onboarding process, slashing processing times to under a minute. slice uses the real-time feature store with MongoDB and ML models to compute over 100 variables instantly, enabling credit eligibility determination in less than 30 seconds. Transforming credit scoring with generative AI Besides the use of alternative data and AI in credit scoring, GenAI has the potential to revolutionize credit scoring and assessment with its ability to create synthetic data and understand intricate patterns, offering a more nuanced, adaptive, and predictive approach. GenAI’s capability to synthesize diverse data sets addresses one of the key limitations of traditional credit scoring – the reliance on historical credit data. By creating synthetic data that mirrors real-world financial behaviors, GenAI models enable a more inclusive assessment of creditworthiness. This transformative shift promotes financial inclusivity, opening doors for a broader demographic to access credit opportunities. Adaptability plays a crucial role in navigating the dynamic nature of economic conditions and changing consumer behaviors. Unlike traditional models which struggle to adjust to unforeseen disruptions, GenAI’s ability to continuously learn and adapt ensures that credit scoring remains effective in real-time, offering a more resilient and responsive tool for assessing credit risk. In addition to its predictive prowess, GenAI can contribute to transparency and interpretability in credit scoring. Models can generate explanations for their decisions, providing clearer insights into credit assessments, and enhancing trust among consumers, regulators, and financial institutions. One key concern however in making use of GenAI is the problem of hallucination, where the model may present information that is either nonsensical or outright false. There are several techniques to mitigate this risk and one approach is using the Retrieval Augment Generation (RAG) approach. RAG minimizes hallucinations by grounding the model’s responses in factual information from up-to-date sources, ensuring the model’s responses reflect the most current and accurate information available. Patronus AI for example leverages RAG with MongoDB Atlas to enable engineers to score and benchmark large language models (LLMs) performance on real-world scenarios, generate adversarial test cases at scale, and monitor hallucinations and other unexpected and unsafe behavior. This can help to detect LLM mistakes at scale and deploy AI products safely and confidently. Another technology partner of MongoDB is Robust Intelligence. The firm’s AI Firewall protects LLMs in production by validating inputs and outputs in real-time. It assesses and mitigates operational risks such as hallucinations, ethical risks including model bias and toxic outputs, and security risks such as prompt injections and personally identifiable information (PII) extractions. As generative AI continues to mature, its integration into credit scoring and the broader credit application systems promises not just a technological advancement, but a fundamental transformation in how we evaluate and extend credit. A pivotal moment in the history of credit The convergence of alternative data, artificial intelligence, and generative AI is reshaping the foundations of credit scoring, marking a pivotal moment in the financial industry. The challenges of traditional models are being overcome through the adoption of alternative credit scoring methods, offering a more inclusive and nuanced assessment. Generative AI, while introducing the potential challenge of hallucination, represents the forefront of innovation, not only revolutionizing technological capabilities but fundamentally redefining how credit is evaluated, fostering a new era of financial inclusivity, efficiency, and fairness. If you would like to discover more about building AI-enriched applications with MongoDB, take a look at the following resources: Digitizing the lending and leasing experience with MongoDB Deliver AI-enriched apps with the right security controls in place, and at the scale and performance users expect Discover how slice enables credit approval in less than a minute for millions

传统信用评分系统存在偏见和歧视、数据考虑有限和可扩展性挑战。银行和贷款机构正在采用人工智能解决这些问题。本文探讨信用评分原理、挑战和人工智能的应用。通过整合替代数据源和机器学习模型,人工智能有潜力重塑信用评分。

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The Verge -

Adobe Acrobat adds generative AI to ‘easily chat with documents’

AI Assistant in Acrobat can recommend questions that you may have about the contents of a document and then answer those inquiries so you don’t have to rummage through the file. | Image: Adobe Adobe is adding a new generative AI experience to its Acrobat PDF management software, which aims to “completely transform the digital document experience” by making information in long documents easier to find and understand. Announced in Adobe’s press release as “AI Assistant in Acrobat,” the new tool is described as a “conversational engine” that can summarize files, answer questions, and recommend more based on the content, allowing users to “easily chat with documents” to get the information they need. It’s available in beta starting today for paying Acrobat users. The idea is that the chatbot will reduce the time-consuming tasks related to working with massive text documents — such as helping students quickly find information for... Continue reading…

Adobe将在Acrobat PDF管理软件中添加AI助手,通过总结文件、回答问题和推荐内容改善数字文档体验。该工具可与所有文档格式一起使用,并在beta版中免费提供给付费用户。

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McKinsey Insights & Publications -

Generative AI in finance: Finding the way to faster, deeper insights

Generative AI technologies can automate time-consuming tasks for finance professionals, but can they be trusted to give the right answers?

22%的大型组织的首席财务官正在积极探索在金融领域使用生成式人工智能(gen AI),而4%正在进行试点。关注点包括安全、隐私、准确性、版权和社会操纵。为了降低风险,公司需要投资于软件开发和数据科学能力,并建立数据质量、模型工程、人机交互和伦理的框架。

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NVIDIA Blog -

Telco GPT: Survey Shows Scale of Industry’s Enthusiasm and Adoption of Generative AI

It’s been five years since the telecommunications industry first deployed 5G networks to drive new performance levels for customers and unlock new value for telcos. But that industry milestone has been overshadowed by the emergence of generative AI and the swift pace at which telcos are embracing large language models as they seek to transform Read Article

电信行业首次部署5G网络,生成式人工智能成为突破性技术,提升收入和节省成本,投资人工智能增长强劲。

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KDnuggets -

Top 5 DataCamp Courses for Mastering Generative AI

Learn the skills you require to kickstart your Generative AI journey with DataCamp - beginner, intermediate, and expert!

DataCamp提供Generative AI课程,帮助个人快速入门。他们有初学者课程,涵盖AI和Generative AI概念,还有中级课程,讲解Large Language Model(LLM)概念和使用OpenAI API。此外,他们还提供专家级课程,教授开发者prompt engineering技巧。DataCamp旨在普及数据技能,为个人提供在现实世界中处理数据所需的技能。

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MongoDB -

Building AI with MongoDB: Navigating the Path From Predictive to Generative AI

It should come as no surprise that the organizations unlocking the largest benefits from generative AI (gen AI) today have already been using predictive AI (a.k.a. classic, traditional, or analytical AI). McKinsey made this same observation back in June 2023 with its “Economic Potential of Generative AI1” research. There would seem to be several reasons for this: An internal culture that is willing to experiment and explore what AI can do Access to skills — though we must emphasize that gen AI is way more reliant on developers than the data scientists driving predictive AI Availability of clean and curated data from across the organization that is ready to be fed into genAI models This doesn’t mean to say that only those teams with prior experience in predictive AI stand to benefit from gen AI. If you take a look at examples from our Building AI case study series, you’ll see many organizations with different AI maturity levels tapping MongoDB for gen AI innovation today. In this latest edition of the Building AI series, we feature two companies that, having built predictive AI apps, are now navigating the path to generative AI: MyGamePlan helps professional football players and coaches improve team performance. Ferret.ai helps businesses and consumers build trust by running background checks using public domain data. In both cases, Predictive AI is central to data-driven decision-making. And now both are exploring gen AI to extend their services with new products that further deepen user engagement. The common factor for both? Their use of MongoDB Atlas and its flexibility for any AI use case. Let's dig in. MyGamePlan: Elevating the performance of professional football players with AI-driven insights The use of data and analytics to improve the performance of professional athletes isn’t new. Typically, solutions are highly complex, relying on the integration of multiple data providers, resulting in high costs and slow time-to-insight. MyGamePlan is working to change that for professional football clubs and their players. (For the benefit of my U.S. colleagues, where you see “football” read “soccer.”) MyGamePlan is used by staff and players at successful teams across Europe, including Bayer Leverkusen (current number one in the German Bundesliga), AFC Sunderland in the English Championship, CD Castellón (current number one in the third division of Spain), and Slask Wroclaw (the current number one in the Polish Ekstraklasa). I met with Dries Deprest, CTO and co-founder at MyGamePlan who explains, “We redefine football analysis with cutting-edge analytics, AI, and a user-friendly platform that seamlessly integrates data from match events, player tracking, and video sources. Our platform automates workflows, allowing coaches and players to formulate tactics for each game, empower player development, and drive strategic excellence for the team's success.” At the core of the MyGamePlay platform are custom, Python-based predictive AI models hosted in Amazon Sagemaker. The models analyze passages of gameplay to score the performance of individual players and their impact on the game. Performance and contribution can be tracked over time and used to compare with players on opposing teams to help formulate matchday tactics. Data is key to making the models and predictions accurate. The company uses MongoDB Atlas as its database, storing: Metadata for each game, including matches, teams, and players. Event data from each game such as passes, tackles, fouls, and shots. Tracking telemetry that captures the position of each player on the field every 100ms. This data is pulled from MongoDB into Python DataFrames where it is used alongside third-party data streams to train the company’s ML models. Inferences generated from specific sequences of gameplay are stored back in MongoDB Atlas for downstream analysis by coaches and players. Figure 1:  With MyGamePlans web and mobile apps, coaching staff, and players can instantly assess gameplay and shape tactics. On selecting MongoDB, Deprest says, We are continuously enriching data with AI models and using it for insights and analytics. MongoDB is a great fit for this use case. “We chose MongoDB when we started our development two years ago. Our data has complex multi-way relationships, mapping games to players to events and tracking. The best way to represent this data is with nested elements in rich document data structures. It's way more efficient for my developers to work with and for the app to process. Trying to model these relationships with foreign keys and then joining normalized tables in relational databases would be slow and inefficient.” In terms of development, Deprest says, “We use the PyMongo driver to integrate MongoDB with our Python ML data pipelines in Sagemaker and the MongoDB Node.js driver for our React-based, client-facing web and mobile apps.” Deprest goes on to say, "There are two key factors that differentiate MongoDB from NoSQL databases we also considered: the incredible level of developer adoption it has, meaning my team was immediately familiar and productive with it. And we can build in-app analytics directly on top of our live data, without the time and expense of having to move it out into some data warehouse or data lake. With MongoDB’s aggregation pipelines, we can process and analyze data with powerful roll-ups, transformations, and window functions to slice and dice data any way our users need it." Moving beyond predictive AI, the MyGamePlan team is now evaluating how gen AI can further improve user experience. Deprest says, "We have so much rich data and analytics in our platform, and we want to make it even easier for players and coaches to extract insights from it. We are experimenting with natural language processing via chat and question-answering interfaces on top of the data. Gen AI makes it easy for users to visualize and summarize the data. We are currently evaluating OpenAI’s ChatGPT LLM coupled with sophisticated approaches to prompt engineering, orchestration via Langchain, and retrieval augmented generation (RAG) using LlamaIndex and MongoDB Atlas Vector Search." As our source data is in the MongoDB Atlas database already, unifying it with vector storage and search is a very productive and elegant solution for my developers. Dries Deprest, CTO and Co-founder, MyGamePlan By building on MongoDB Atlas, MyGamePlan’s team can use the breadth of functionality provided by a developer data platform to support almost any application and AI needs in the future. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. Ferret.ai: Building trust with relationship intelligence powered by AI and MongoDB Atlas while cutting costs by 30% Across the physical and digital world, we are all constantly building relationships with others. Those relationships can be established through peer-to-peer transactions across online marketplaces, between tradespeople and professionals with their prospective clients, between investors and founders, or in creating new personal connections. All of those relationships rely on trust to work, but building it is hard. Ferret.ai was founded to remove the guesswork from building that trust. Ferret is an AI platform architected from the ground up to empower companies and individuals with real-time, unbiased intelligence to identify risks and embrace opportunities. Leveraging cutting-edge predictive and generative AI, hundreds of thousands of global data sources, and billions of public documents, Ferret.ai provides curated relationship intelligence and monitoring — once only available to the financial industry — making transparency the new norm. Al Basseri, CTO at Ferret tells us how it works: "We ingest information about individuals from public sources. This includes social networks, trading records, court documents, news archives, corporate ownership, and registered business interests. This data is streamed through Kafka pipelines into our Anyscale/Ray MLops platform where we apply natural language processing through our spaCy extraction and machine learning models. All metadata from our data sources — that's close to three billion documents — along with inferences from our models are stored in MongoDB Atlas. The data in Atlas is consumed by our web and mobile customer apps and by our corporate customers through our upcoming APIs." Figure 2:  Artificial intelligence + real-time data = Relationship Intelligence from Ferret.ai. Moving beyond predictive AI, the company’s developers are now exploring opportunities to use gen AI in the Ferret platform. "We have a close relationship with the data science team at Nvidia,” says Basseri. “We see the opportunity to summarize the data sources and analysis we provide to help our clients better understand and engage with their contacts. Through our experimentation, the Mistral model with its mixture-of-experts ensemble seems to give us better results with less resource overhead than some of the larger and more generic large language models." As well as managing the data from Ferret’s predictive and gen AI models, customer data and contact lists are also stored in MongoDB Atlas. Through Ferret’s continuous monitoring and scoring of public record sources, any change in an individual's status is immediately detected. As Basseri explains, "MongoDB Atlas Triggers watch for updates to a score and instantly send an alert to consuming apps so our customers get real-time visibility into their relationship networks. It's all fully event-driven and reactive, so my developers just set it and forget it." Basseri also described the other advantages MongoDB provides his developers: Through Atlas, it’s available as a fully managed service with best practices baked in. That frees his developers and data scientists from the responsibilities of running a database so they can focus their efforts on app and AI innovation MongoDB Atlas is mature, having seen it scale in many other high-growth companies The availability of engineers who know MongoDB is important as the team rapidly expands Beyond the database, Ferret is extending its use of the MongoDB Atlas platform into text search. As the company moves into Google Cloud, it is migrating from its existing Amazon OpenSearch service to Atlas Search. Discussing the drivers for the migration, Basseri says, "Unifying both databases and search behind a single API reduces cognitive load for my developers, so they are more productive and build features faster. We eliminate all of the hassle of syncing data between database and search. Again, this frees up engineering cycles. It also means our users get a better experience because previous latency bottlenecks are gone — so as they search across contacts and content on our platform, they get the freshest results, not stale and outdated data." By migrating from OpenSearch to Atlas Search, we also save money and get more freedom. We will reduce our total cloud costs by 30% per month just by eliminating unnecessary data duplication between the database and the search engine. And with Atlas being multi-cloud, we get the optionality to move across cloud providers as and when we need to. Al Basseri, CTO at Ferret.ai Once the migration is complete, Basseri and the team will begin development with Atlas Vector Search as they continue to build out the gen AI side of the Ferret platform. What's next? No matter where you are in your AI journey, MongoDB can help. You can get started with your AI-powered apps by registering for MongoDB Atlas and exploring the tutorials available in our AI resources center. Our teams are always ready to come and explore the art of the possible with you. 1 https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier

该文章讨论了两家公司如何利用预测性人工智能构建应用,并探索生成性人工智能的潜力。MyGamePlan利用数据和分析改善职业足球运动员的表现,使用MongoDB Atlas作为数据库存储数据。Ferret.ai通过使用AI和MongoDB Atlas构建关系智能平台,提供实时的、无偏的情报,帮助建立信任关系。两家公司都在评估生成性人工智能对于提升用户体验的潜力。

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IBM Blog -

6 ways generative AI can optimize asset management

6 ways generative AI can optimize your enterprise asset management operations, including field service, maintenance and compliance. The post 6 ways generative AI can optimize asset management appeared first on IBM Blog.

资产管理者面临的任务是简化维护计划、提高资产或设备可靠性、优化工作流程以提高质量和生产力。生成式人工智能(AI)可以优化企业资产管理操作,包括现场服务、维护和合规性。IBM Consulting®正在进行生成式AI的研究。

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The Verge -

Tim Cook confirms Apple’s generative AI features are coming ‘later this year’

Photo by Chris Welch / The Verge During Apple’s quarterly earnings call on Thursday afternoon, CEO Tim Cook mentioned that the company is working on generative AI software features that will make their way to customers “later this year.” That aligns with reporting from Bloomberg’s Mark Gurman, who said recently that iOS 18 could be the “biggest” update in the operating system’s history. Cook’s teases — he mentioned generative AI several times, but never got specific — seem to confirm that we’re in for a big release this fall. “As we look ahead, we will continue to invest in these and other technologies that will shape the future. That includes artificial intelligence, where we continue to spend a tremendous amount of time and effort, and we’re excited to share the... Continue reading…

苹果公司CEO蒂姆·库克透露公司将在今年晚些时候推出生成式人工智能(AI)软件功能,与彭博社记者的报道相吻合。这表明苹果有雄心将AI整合到其软件平台中。

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The Verge -

Google Maps is getting ‘supercharged’ with generative AI

Image: Google Google is bringing generative AI to — where else? — Google Maps, promising to help users find cool places through the use of large language models (LLM). The feature will answer queries for restaurant or shopping recommendations, for example, using its LLM to “analyze Maps’ detailed information about more than 250 million places and trusted insights from our community of over 300 million contributors to quickly make suggestions for where to go.” Google says the feature will first become available in the US, but there’s no word yet on when other countries will also get it. It’s a natural place for Google to flex its AI muscles It’s a natural place for Google to flex its AI muscles, as the company has been laboring for years to turn its... Continue reading…

谷歌将在谷歌地图中引入生成式人工智能,帮助用户发现有趣的地点。该功能将分析超过2.5亿个地点的详细信息和3亿多贡献者的见解,提供快速的餐厅和购物推荐。首先在美国推出,其他国家尚未确定。谷歌旨在将导航产品转变为发现新地点的搜索工具。

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The Keyword -

Try ImageFX and MusicFX, our newest generative AI tools in Labs

A collection of generated images made using ImageFX.

Google发布了ImageFX,这是一款由Imagen 2驱动的新图像生成工具。他们还改进了现有的生成AI创作工具MusicFX和TextFX。ImageFX允许用户通过简单的文本提示创建图像,并提供了带有“表达性芯片”的界面,方便快速实验。MusicFX现在允许用户创建更长的曲调和音乐循环,并且更新后的版本音频质量更高。TextFX也进行了可用性更新。Google努力确保这些工具的安全和隐私,包括添加过滤器以限制问题输出,并使用SynthID水印进行识别。这些工具在美国、新西兰、肯尼亚和澳大利亚可用。

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