What level of impact could AI have on the government's spending?
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After many conversations with both clients and candidates around where the Data market will be moving in 2024 recently, it has become obvious that organisations are really trying to upscale their use of AI and Machine Learning models moving forward. While the above seems like an obvious statement, what isn’t obvious is the challenges that businesses face to not only best utilise AI and Machine Learning models, but also to stay within ever changing governance and legislation principles that the use of AI brings. Furthermore, this changing AI landscape seems to present issues on how, and who should be policing the use of AI in general. This concern can be directly shown from the recent “AI safety summit” hosted by the British government, which took place for the first time ever on the 1st and 2nd of November. Clearly (at least from my perspective) this shows that major powers are seriously taking note of the potential implications of the large-scale introduction of AI into modern society. Many AI laws will no doubt be introduced in the following years to regulate the increased use of AI, however on the other side, increased use will drive competition between the creators of these AI models (ChatGPT, Anthropic etc.), which will only further improve the sophistication and expand the utilisation of what AI can do. As a “non-techy” it seems that there are two opposing ideals here, one side that wants to regulate the use of AI and one side that will be driving for its perpetual growth. This raises multiple questions: - Should governments be able to implement major regulation on AI? - Who should be regulating the use of AI? - Should there be a limit to how far AI can progress? I am sharing this to my network as I am super keen to expand my knowledge of the AI space. I’d love to hear thoughts/discussion, from those on here who are more well-versed in this topic – if you can teach me anything it would be much appreciated! #artificialintelligence #ai #dataanalytics #data #datagovernance #azure
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Principal, EY. Government and Public Sector Department of Homeland Security Account Leader. | Digital Transformation | Data-Enabled Solutions | Innovation & Modernization
A recent White House memo developed by the Biden administration acknowledges the role that #AI plays in transforming government functions and public services. It's great to see how the government is not only harnessing AI for more efficient and effective operations but will be developing the necessary regulations and guidelines. https://lnkd.in/eQ8UpjHP #Government
Using AI to improve government functions among top Biden administration R&D priorities for fiscal 2025
https://fedscoop.com
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Survey finds companies are experimenting with AI in financial reporting. Survey finds companies are experimenting with AI in financial reporting. https://hubs.la/Q02yvLJ_0
Survey finds companies are experimenting with AI in financial reporting
lexpert.ca
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The public sector has a high potential for artificial intelligence (AI) to have a transformative impact. After all, governments have access to tremendous amounts of data, and government operations affect each of us in small and large ways every day. Learn more here: https://lnkd.in/d6wD5e-q
AI in government: The path to adoption and deployment
sas.com
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Executive Director - UK at the Institute of Financial Accountants, supporting SMEs and SMP practices.07711955939
Really useful article and AI is a topic that grows in significance and acceptability. If you find the article useful then subscribe for free to The Institute of Financial Accountants digital platform. Lots of helpful content. And want more around AI, then register for our conference on 25 June to learn more from Tushir Patel https://lnkd.in/eQvbkdhP
Nine AI tools to help accountants solve challenges common in small firms, boost job quality and efficiency, and build skills. https://smpl.is/932pw
9 AI tools to help you solve problems now
financialaccountant.co.uk
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Using AI to improve government functions and public services is a key research and development priority for the Biden administration for fiscal 2025, according to a White House strategy memo published Thursday. #AI #governmenttechnology #ITmodernization https://bit.ly/3qBmv4M
Using AI to improve government functions among top Biden administration R&D priorities for fiscal 2025
https://fedscoop.com
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Lots of talk about AI, but are agencies spending money on it?: Federal agencies are looking at different ways to utilize artificial intelligence. But just how much of their budgets are agency leaders willing to put AI? The post Lots of talk about AI, but are agencies spending money on it? first appeared on Federal News Network. @Poseidon-US #FedearlNewsRadio #News
Lots of talk about AI, but are agencies spending money on it?
https://federalnewsnetwork.com
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#GenAI is all the rage, but what does that mean for public sector organizations specifically? We examined more than 19,000 tasks collected by the US Department of Labor so you don't have to. This interactive article seeks to cut through the uncertainty and get the broadest-possible perspective on how and where generative AI can impact government work, revealing three categorizations that can help government leaders make informed, strategic decisions about how to implement generative AI in their organizations.
Generative AI and government work: An in-depth analysis of 19,000 tasks
www2.deloitte.com
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Hypothesis: We will see the emergence of domain specific AI-as-a-judge systems that increasingly narrow down and specialize in evaluating/monitoring AI agents on specific things. This allows companies building domain specific AI-as-a-judge systems to focus on evaluating their evaluations, while allowing broader enterprises/builders to consume it as a product. These companies can also specialize on the cost, latency, and infrastructure, thus driving down overall evaluation cost.
A big issue I see with AI systems is that people aren't spending enough time evaluating their evaluation pipeline. 1. Most teams use more than one metrics (3-7 metrics in general) to evaluate their applications, which is a good practice. However, very few are measuring the correlation between these metrics. If two metrics are perfectly correlated, you probably don't need both of them. If two metrics strongly disagree with each other, either this reveals something important about your system, or your metrics just aren't trustworthy. 2. Many (I estimate 60 - 70%?) use AI to evaluate AI responses, with common criteria being conciseness, relevance, coherence, faithfulness, etc. I find AI-as-a-judge very promising, and expect to see more of this approach in the future. However, AI-as-a-judge scores aren’t deterministic the way classification F1 scores or accuracy are. They depend on the model, the judge's prompt, and the use case. Many AI judges are good, but many are bad. Yet, very few are doing experiments to evaluate their AI judges. Are good responses given better scores? How reproducible the scores are -- if you ask the judge twice, do you get the same score? Is the judge's prompt optimal? Some aren’t even aware of the prompts their applications are using, because they use prompts created by eval tools or by other teams. Also fun fact I learned from a (small) poll yesterday: some teams are spending more money on evaluating models’ responses than on generating responses 🤯 #aiengineering #llms #aievaluation
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