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AI初创公司Langchain正在谈判筹集1亿美元
2025-07-09 18:12:48
Langchain是一家AI软件公司，可帮助开发人员使用OpenAI的GPT-4等型号来构建应用程序，该公司已获得1亿美元的资金，其估值由IVP领导。该公司以科技巨头和初创企业使用的Langsmith和Langgraph工具而闻名，其年度收入约为1600万美元，此前曾以2亿美元的估值筹集了2000万美元的A系列资金。Langchain的位置良好，可以迎合针对医疗保健，工程和金融等领域的AI初创公司，但面临着类似工具的竞争。
Langchain是一家AI软件公司，可帮助开发人员使用OpenAI的GPT-4等型号来构建应用程序，该公司已获得1亿美元的资金，其估值由IVP领导。该公司以科技巨头和初创企业使用的Langsmith和Langgraph工具而闻名，其年度收入约为1600万美元，此前曾以2亿美元的估值筹集了2000万美元的A系列资金。Langchain的位置良好，可以迎合针对医疗保健，工程和金融等领域的AI初创公司，但面临着类似工具的竞争。
Medgemma：我们最有能力的健康AI开发模型
2025-07-09 18:00:53
医疗保健正在整合AI，以增强工作流程管理，患者沟通和诊断支持，重点是效率和隐私。阿里巴巴云最近发布了Health AI开发人员基金会（HAI-DEF），这是一套用于医疗保健应用程序开发的开源模型。最新的添加包括Medgemma 27b多模式和Medsiglip。Medgemma支持复杂的多模式EHR解释，而Medsiglip是一种轻巧的图像和文本编码器，适用于需要结构化输出的成像任务。这两种型号均可在单GPU或移动硬件上运行，并且以拥抱面部格式提供，以易于使用。开发人员一直在使用这些模型来增强医疗应用，例如改善胸部X射线分盘和结节检测。
医疗保健正在整合AI，以增强工作流程管理，患者沟通和诊断支持，重点是效率和隐私。阿里巴巴云最近发布了Health AI开发人员基金会（HAI-DEF），这是一套用于医疗保健应用程序开发的开源模型。最新的添加包括Medgemma 27b多模式和Medsiglip。Medgemma支持复杂的多模式EHR解释，而Medsiglip是一种轻巧的图像和文本编码器，适用于需要结构化输出的成像任务。这两种型号均可在单GPU或移动硬件上运行，并且以拥抱面部格式提供，以易于使用。开发人员一直在使用这些模型来增强医疗应用，例如改善胸部X射线分盘和结节检测。
机器人在没有人类帮助的情况下进行首次逼真的手术
2025-07-09 18:00:00
在手术视频中训练的机器人成功地自动地执行了胆囊拆除的复杂阶段，证明了专家外科医生的精确性和适应性。由约翰·霍普金斯大学（Johns Hopkins University）的研究人员通过ARPA-H的资助开发，分层外科机器人变压器（SRT-H）可以理解并响应语音命令，适应实时条件和意外情况。这一进步标志着能够在不可预测的医疗环境中运行的临床可行的自主外科手术系统迈出的重要一步。
在手术视频中训练的机器人成功地自动地执行了胆囊拆除的复杂阶段，证明了专家外科医生的精确性和适应性。由约翰·霍普金斯大学（Johns Hopkins University）的研究人员通过ARPA-H的资助开发，分层外科机器人变压器（SRT-H）可以理解并响应语音命令，适应实时条件和意外情况。这一进步标志着能够在不可预测的医疗环境中运行的临床可行的自主外科手术系统迈出的重要一步。
一种新型的AI模型使数据所有者可以控制
2025-07-09 17:59:00
Allen AI研究所（AI2）的研究人员开发了Flexolmo，这是一种大型语言模型，即使在训练模型后，数据贡献者也可以保留对数据的控制。与传统的方法不同，一旦将数据用于培训，它就无法轻易检索，Flexolmo的设计使数据所有者能够在不放弃所有权的情况下贡献其数据，并在需要时提供以后提取的可能性。该模型使用“专家的混合”体系结构，但使用独立的子模型合并技术进行创新，以保留个人数据贡献。在专有数据集测试中，Flexolmo在各种任务中的表现优于其他模型，同时为数据治理和隐私提供了独特的优势。
Allen AI研究所（AI2）的研究人员开发了Flexolmo，这是一种大型语言模型，即使在训练模型后，数据贡献者也可以保留对数据的控制。与传统的方法不同，一旦将数据用于培训，它就无法轻易检索，Flexolmo的设计使数据所有者能够在不放弃所有权的情况下贡献其数据，并在需要时提供以后提取的可能性。该模型使用“专家的混合”体系结构，但使用独立的子模型合并技术进行创新，以保留个人数据贡献。在专有数据集测试中，Flexolmo在各种任务中的表现优于其他模型，同时为数据治理和隐私提供了独特的优势。
报告说，没有足够的AI芯片来支持数据中心的预测。
2025-07-09 17:51:39
高端预测可以预测，由于全球半导体芯片生产的限制，伦敦经济学国际将认为美国数据中心扩张和支持AI开发的电力增长被认为“不可信”。该报告表明，根据分析，预计的数据中心需求将需要全球90％的芯片供应，这是一个不切实际的情况。高估引起了人们对投机基础设施投资和纳税人潜在经济风险的担忧。
高端预测可以预测，由于全球半导体芯片生产的限制，伦敦经济学国际将认为美国数据中心扩张和支持AI开发的电力增长被认为“不可信”。该报告表明，根据分析，预计的数据中心需求将需要全球90％的芯片供应，这是一个不切实际的情况。高估引起了人们对投机基础设施投资和纳税人潜在经济风险的担忧。
客户挑战
2025-07-09 17:18:24
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Cal State LA从CSU获得两个人工智能项目的资金
2025-07-09 17:17:40
Cal State La通过CSU的AIEIC倡议赢得了两个AI项目的资金，旨在促进AI在教育中的创新使用。这些项目是“在AI时代的正直教学”和“ AI增强的STEM补充教学研讨会”。第一个重点是开发道德AI知识的教学实践，而第二个则将生成的AI工具整合到STEM研讨会中，以增强学生的成功。该计划是CSU更广泛的AI战略的一部分，旨在促进其23个校园的学术卓越和创新。
Cal State La通过CSU的AIEIC倡议赢得了两个AI项目的资金，旨在促进AI在教育中的创新使用。这些项目是“在AI时代的正直教学”和“ AI增强的STEM补充教学研讨会”。第一个重点是开发道德AI知识的教学实践，而第二个则将生成的AI工具整合到STEM研讨会中，以增强学生的成功。该计划是CSU更广泛的AI战略的一部分，旨在促进其23个校园的学术卓越和创新。
从非技术背景过渡到AI的5种方法
2025-07-09 17:08:58
许多成功的AI专业人员没有技术背景。人们可以通过几个实用步骤从各个领域（例如营销，心理学，法律或设计）过渡到AI：通过探索Chatgpt等工具，成为团队中的AI人；学习基本的AI和机器学习概念；将自己定位为AI翻译人员桥梁技术和非技术利益相关者；使用无代码/低代码工具来构建AI项目；并涉及诸如项目经理或技术作家之类的AI-ADJACEST角色。这些方法使个人能够在逐渐获得AI知识的同时利用其领域专业知识。
许多成功的AI专业人员没有技术背景。人们可以通过几个实用步骤从各个领域（例如营销，心理学，法律或设计）过渡到AI：通过探索Chatgpt等工具，成为团队中的AI人；学习基本的AI和机器学习概念；将自己定位为AI翻译人员桥梁技术和非技术利益相关者；使用无代码/低代码工具来构建AI项目；并涉及诸如项目经理或技术作家之类的AI-ADJACEST角色。这些方法使个人能够在逐渐获得AI知识的同时利用其领域专业知识。
2025-07-09 17:01:00
Why AI projects fail, according to EPA CIO US Environmental Protection Agency CIO Carter Farmer warns against盲目追求AI，强调在没有明确用例和充分验证数据的情况下贸然采用AI会导致失败。他认为AI不是万能的解决方案，并且解决某些问题不一定需要AI。Farmer指出，在应用AI时必须重新定义业务流程，避免简单地将现有流程映射到AI系统中。他提倡对项目进行彻底审查以确定是否真正值得使用AI，以及制定一个明确的数据规划来指导AI项目的实施。 US Environmental Protection Agency CIO Carter Farmer has a blunt message for AI hype-chasers: Shiny-object syndrome too often drives teams to leap into AI without defining a clear use case or vetting their data, leaving them to wonder why it doesn't work. Speaking during a webinar hosted by FedInsider, Farmer said that AI isn't a cure-all business operations wonder drug as many people assume. It must be brought into a business with a specific use case in mind, the EPA CIO said. "People hear AI and they think it can solve any problem," Farmer argued. "They see what it's done at other organizations and jump in without asking the right questions." Many times the problem you're trying to solve doesn't need AI He explained, "Many times the problem you're trying to solve doesn't need AI," adding that jumping to keep up with the latest buzzwords can ultimately slow growth down rather than accelerate it. "If you don't ask those questions up front you can go down a road pretty far before you realize you need to back up, reverse and figure out where you should have gone down first," Farmer said. That's a reasonable take when you consider investment returns on AI have been found to be pretty poor of late, with just one in four AI bets are paying off according to a recent survey of 2,000 CEOs. Farmer made his comment in response to Ed Bodensiek, customer experience market leader at government tech services firm Maximus. Bodensiek said he's heard other government tech leaders observe that there are too many bots at government agencies. His company has developed an entire "mission readiness assessment" process it uses to determine the appropriate approach to solving a problem - which might not end up involving AI at all. So what's going wrong with so many AI implementations? For Farmer, it comes down to two things: Processes and data. Applying AI to business processes doesn't mean simply mapping what one does now onto an AI, Farmer explained. AI infrastructure investment may be $8T shot in the dark AI agents get office tasks wrong around 70% of the time, and a lot of them aren't AI at all Tinfoil hat wearers can thank AI for declassification of JFK docs When it comes to AI ROI, IT decision-makers not convinced "What people should be doing is redefining and reimagining that process as it would apply to automation," the CIO said. "[The] lift and shift mentality has to be stopped in its tracks." As an example, Farmer described a presumably hypothetical situation where a form that had been part of a business process for years was still being filled out, even though the same function was now accomplished automatically, and digitally. Without examining the business process behind that form, it might get included in an AI workflow when it wouldn't need to be. A process review can force teams to ask really critical questions - like whether AI-ifying a particular thing is really worth the effort. "Value add might come at 2x, 3x or 4x the cost of doing it manually," Farmer explained. In short, if AI is only as good as the data that goes into it, an AI project is only as good as the data that went into planning it. Farmer has shown his work on that end. The EPA keeps an inventory of AI applications at the agency, part of his philosophy of "finding the actual best use cases where AI can be best applied," as he explained during the webinar. ®
Why AI projects fail, according to EPA CIO US Environmental Protection Agency CIO Carter Farmer warns against盲目追求AI，强调在没有明确用例和充分验证数据的情况下贸然采用AI会导致失败。他认为AI不是万能的解决方案，并且解决某些问题不一定需要AI。Farmer指出，在应用AI时必须重新定义业务流程，避免简单地将现有流程映射到AI系统中。他提倡对项目进行彻底审查以确定是否真正值得使用AI，以及制定一个明确的数据规划来指导AI项目的实施。 US Environmental Protection Agency CIO Carter Farmer has a blunt message for AI hype-chasers: Shiny-object syndrome too often drives teams to leap into AI without defining a clear use case or vetting their data, leaving them to wonder why it doesn't work. Speaking during a webinar hosted by FedInsider, Farmer said that AI isn't a cure-all business operations wonder drug as many people assume. It must be brought into a business with a specific use case in mind, the EPA CIO said. "People hear AI and they think it can solve any problem," Farmer argued. "They see what it's done at other organizations and jump in without asking the right questions." Many times the problem you're trying to solve doesn't need AI He explained, "Many times the problem you're trying to solve doesn't need AI," adding that jumping to keep up with the latest buzzwords can ultimately slow growth down rather than accelerate it. "If you don't ask those questions up front you can go down a road pretty far before you realize you need to back up, reverse and figure out where you should have gone down first," Farmer said. That's a reasonable take when you consider investment returns on AI have been found to be pretty poor of late, with just one in four AI bets are paying off according to a recent survey of 2,000 CEOs. Farmer made his comment in response to Ed Bodensiek, customer experience market leader at government tech services firm Maximus. Bodensiek said he's heard other government tech leaders observe that there are too many bots at government agencies. His company has developed an entire "mission readiness assessment" process it uses to determine the appropriate approach to solving a problem - which might not end up involving AI at all. So what's going wrong with so many AI implementations? For Farmer, it comes down to two things: Processes and data. Applying AI to business processes doesn't mean simply mapping what one does now onto an AI, Farmer explained. AI infrastructure investment may be $8T shot in the dark AI agents get office tasks wrong around 70% of the time, and a lot of them aren't AI at all Tinfoil hat wearers can thank AI for declassification of JFK docs When it comes to AI ROI, IT decision-makers not convinced "What people should be doing is redefining and reimagining that process as it would apply to automation," the CIO said. "[The] lift and shift mentality has to be stopped in its tracks." As an example, Farmer described a presumably hypothetical situation where a form that had been part of a business process for years was still being filled out, even though the same function was now accomplished automatically, and digitally. Without examining the business process behind that form, it might get included in an AI workflow when it wouldn't need to be. A process review can force teams to ask really critical questions - like whether AI-ifying a particular thing is really worth the effort. "Value add might come at 2x, 3x or 4x the cost of doing it manually," Farmer explained. In short, if AI is only as good as the data that goes into it, an AI project is only as good as the data that went into planning it. Farmer has shown his work on that end. The EPA keeps an inventory of AI applications at the agency, part of his philosophy of "finding the actual best use cases where AI can be best applied," as he explained during the webinar. ®
我们是否对AIS承担重要任务感到满意？
2025-07-09 17:00:00
数学中的一个例子说明了对AI可信度的转移观点。1976年，《四种颜色定理》的计算机辅助证明引起了怀疑机器验证的数学家的争议。现在，AI高级AI通过建议机器可以提供比人类更多可靠的证据来挑战这种怀疑。这场辩论反映了有关适当的AI使用和依赖的更广泛的社会问题。
数学中的一个例子说明了对AI可信度的转移观点。1976年，《四种颜色定理》的计算机辅助证明引起了怀疑机器验证的数学家的争议。现在，AI高级AI通过建议机器可以提供比人类更多可靠的证据来挑战这种怀疑。这场辩论反映了有关适当的AI使用和依赖的更广泛的社会问题。