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jack_fox

Ivan Ostojic: Looking forward with AI - 0 views

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    'There will be some disillusionment for people who thought this is a panacea, where you don't need any data structure because this is an intelligent technology. There is a lot of marketing hype that is creating that perception. But when you see what serious enterprises are doing, they never went big on implementing this technology because they were worried about the hallucinations and so forth. We are seeing very cautious adoption, where people are mainly trying to do use cases that are more internal - like knowledge summaries, quick ways for employees to find answers. Most of them are cautious about exposing this technology to customers because of the cases that came into the press like the Air Canada case.'
jack_fox

Billions of Google redirects to stop working next year - 0 views

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    News that supports a fresh round of Inlink Restoration service sales
Rob Laporte

The state of AI in early 2024 | McKinsey - 0 views

  • services.2Includes respondents working for organizations focused on human resources, legal services, management consulting, market research, R&D, tax preparation, and training. Exhibit 1
  • Also, responses suggest that companies are now using AI in more parts of the business. Half of respondents say their organizations have adopted AI in two or more business functions, up from less than a third of respondents in 2023 (Exhibit 2)
  • The biggest increase from 2023 is found in marketing and sales, where reported adoption has more than doubled. Yet across functions, only two use cases, both within marketing and sales, are reported by 15 percent or more of respondents.
Rob Laporte

Large language models as tax attorneys: a case study in legal capabilities emergence | ... - 0 views

  • LLM prompting involves designing text inputs to generate a response from an LLM. The goal of prompting is to steer the behaviour of the LLM in a way that elicits a desired outcome. Recent research has focused on developing effective prompting techniques that can expand LLMs' capabilities when carrying out a variety of tasks. Examples include prompt patterns [21], in-context instruction learning [22], evolutionary prompt engineering [23] and domain-specific keywords with a trainable gated prompt to guide toward a target domain for general-domain LLMs [24]. Zhong et al. [25] experiment with prompting LLMs to do scientific tasks across fields like business, science, and health by providing the LLM with a research goal and two large corpora, asking the LLM for corpus-level difference. Reppert et al. [26] develop iterated decomposition, a human-in-the-loop workflow for developing and refining compositional LLM programs that improves performance on real-world science question and answer tasks.
jack_fox

ChatGPT prompts for SEO: What you need to know - 0 views

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    'it's often the case that if you feel like you're down a rabbit hole with ChatGPT and aren't getting the data or responses you want, you may want to consider starting fresh and better orienting the tool to what you want.'
jack_fox

iOS 18: Brace yourself for the new Apple Mail inbox - 0 views

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    re email marketing following update: 'make sure anything you would have included in the pre-header is spic and span for AI consumption'
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