Deep research: the next generation of Artificial Intelligence
What's the impact of artificial intelligence on energy demand?
When search engines were invented, they simply scanned the internet and came up with a list of the most popular, relevant sites. Then came ‘generative AI’, which creates new text and images from the search results by accumulating vast quantities of text or images. It uses this to find answers, not as a human would do, by understanding the question, but simply by identifying patterns in the data. Many of us now routinely use this without even being aware of it. It is built into Google and Microsoft Office, and most of the time, it is pretty reliable. It is predicted that worldwide, nearly 400 million people will use AI this year, a 20% rise on 2024.
Agentic AI and Deep Research
'Agentic' AI does not merely answer questions. It takes text, images, sounds, and other forms of data; manipulates them with a range of tools; collaborates with other AI agents, and learns from past experiences to improve over time. Sixty percent of advertising agencies already use AI to create advertisements, and agentic AI is expected to be able to manage entire marketing campaigns: creating, testing, and adjusting creative materials automatically to maximize audience engagement. Meanwhile, such AI agents are already curating news feeds based on user preferences, summarizing lengthy articles, and generating video highlights for breaking news events. In Italy last month, we saw the first magazine edition produced entirely by AI, based only on prompts from journalists.
'Deep research' tools – Open AI’s Chat GPT, Google’s Gemini, and the latest arrival, Perplexity – apply these techniques to more and more current data, including research papers, databases, and live web data. They break down complex queries, check their understanding of the question, analyze data, and synthesize the results into concise reports, with summaries, citations, and visuals. They can identify changing trends and deeper context.
There are also environmental issues. AI needs vast computer power to process data, so the data farms which provide this require a lot of electricity. A planned new data center in Slough will consume twice as much electricity as Heathrow airport at its peak. They also need clean water for cooling, typically two million liters a day, equivalent to the water consumption of 14,000 people. In a dry region like East Anglia, where water is already scarce, this is a significant figure.
We decided to test Perplexity on a controversial issue which we have covered in the past in East Anglia Bylines. We asked this question: "There is controversy about how to connect North Sea wind farms to the national grid. What are the arguments, and what is public opinion?"
It took Perplexity four minutes to generate an 1800-word report, breaking down the task into three stages, and reporting what it was doing at each stage. It consulted and listed 47 sources, all authoritative and relevant. They included government, media, academic, and industry sites, and political documents from several parties. A human researching the same question could have found them all, but pressed for time, would probably not have done so.
'Deep research' tools – Open AI’s Chat GPT, Google’s Gemini, and the latest arrival, Perplexity – apply these techniques to more and more current data, including research papers, databases, and live web data. They break down complex queries, check their understanding of the question, analyze data, and synthesize the results into concise reports, with summaries, citations, and visuals. They can identify changing trends and deeper context.
Testing Perplexity on a Controversial Issue
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The report’s subheadings give a sense of its coverage:
- Grid connection and policy disputes
- Delays and queue issues
- Regulatory responses
- Competing Infrastructure Proposals: Onshore vs. Offshore Solutions
- Onshore infrastructure approach
- Offshore grid alternative
- Community and environmental concerns
- Local opposition and environmental impact
- Economic and tourism concerns
- Political dimensions
- Public opinion on wind farms and grid connections
- Support for wind farm development
- Information gaps and communication needs
- Balancing competing interests: the path forward
- Coordinated approach
- Regulatory reform and streamlining
- Conclusion
- References
It was a solid report, covering the main issues at least as well as a human journalist would do. But there were weaknesses. Greenpeace was the only site associated directly with protest groups, probably reflecting the fact that such groups have a smaller online presence, or perhaps were regarded as less authoritative. However, the report records significant public opposition in Suffolk, citing an MP, the chairman of Aldeburgh Museum and ‘a local campaigner’, though curiously, the word ‘pylon’ does not appear anywhere. It was thin on the offshore grid options which have attracted much local debate.
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At first glance, the report appears thin on public opinion surveys. The only one cited was about onshore wind farms in Northumberland, which was not strictly relevant to the question, probably because the question did not make it clear that ‘North Sea wind farms’ meant offshore ones.
The test of Perplexity confirms that Deep Research AI can produce very competent reports extremely quickly. But some issues have been widely noted. Where there are gaps in data, such tools can produce ‘hallucinations’, creating plausible but entirely fictitious reports. Similarly, the AI can only read what is there, and available online data will tend to reflect the cultural interests and biases of the world when it was created.
Environmental and Employment Implications
And, of course, there are employment issues in any industry dependent on written or visual material. A vast range of jobs will be affected. Some may become richer, but many will just disappear. But this process is probably unstoppable. Only where individual creativity is highly prized may people escape the effects.
AI is an invaluable tool when used with care. At East Anglia Bylines, we use AI as a research tool, to identify sources and evidence, or to summarize long or complex documents. We sometimes use it to suggest or refine headlines or tweets.
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