As an authority in the sector, what critical challenges do you think the AI community needs to deal with to make sure responsible & and ethical AI deployment?

The people behind your data matter. We have to be sure that we now have a various workforce providing the human intelligence that makes artificial intelligence higher. It’s critical we be sure that individuals are representative of the population if we’re to have accurate and unbiased AI. And also that we’re protecting human dignity as we develop these tools. It’s necessary that we develop AI systems that augment people and do not replace them. The goal must be making humans more efficient or more productive. That’s key for developing a prosperous future.

How has AI impacted your specific field of experience, and what transformative changes do you foresee within the near future?
  

There’s been positive and more difficult changes, and I’ll start with the more difficult. We’ve seen a rise in the usage of AI by participants in online research, which is reducing data quality. Essentially, persons are using AI to enhance their very own responses in a way that was not intended by the researcher. On the nice side, there’s massive value in research summarisation and understanding what’s been done in the sector already. Being capable of summarise and explain research means you now not should be an authority to know academic papers – you may get AI to elucidate it to you.

 

How do you envision AI shaping various industries, and what advice would you give to businesses searching for to integrate AI into their operations?

  

The most diverse use case of AI goes to be augmenting people’s day-to-day use – primarily as a writing and communication tool. In terms of recommendation, look first to enhance people’s productivity quite than pondering of AI as a substitute for human intelligence. Understand the source of the information used to coach these tools, ensuring it’s sufficient on your use case. And give clear guardrails for a way you would like people to make use of AI – how much private data could be uploaded? What training clarity is accessible on how best to make use of these tools?

 

In your opinion, what opportunities and challenges does AI present for job markets and workforce development worldwide?

Roles that leverage AI and human collaboration will rise. The earliest type of that will be Prompt Engineers, or people who find themselves experts at maximising the outputs of AI tools. Again, it’s necessary that it’s a productivity enhancer, not a human substitute. Related to that, helping people do their job effectively alongside AI is crucial. In certain industries – and knowledge staff are particularly affected by this – there’s a have to re-skill people, training them in find out how to utilise AI to do their jobs more effectively.

 

Can you share an example of an AI application or project that has personally impressed you, and explain why it stands out?

  

Hume AI has worked on gathering speech and emotion data from people, designing a toolkit that understands human emotional expression. They did a really cool demo recently taking a look at the song “Used To Be Young” by Miley Cyrus, mapping her emotional expressions throughout the video to predict her pain, joy, fear, relief and so forth. That’s a robust example since it uses AI to know human behaviour beyond just Natural Language Processing.

 

 

 

What measures do you think must be taken to bridge the AI research gap between developed and developing nations to make sure equitable technological progress?

  

To address the AI research gap between developed and developing nations, two key strategies are pivotal: Access to Quality Data and Inclusive Research. First, equalising access to datasets is crucial – open science brings more people into the community. Open-source initiatives can democratise this access, empowering researchers globally. Second, it’s crucial to encourage an inclusive research ecosystem by involving participants and researchers from diverse and sometimes marginalised backgrounds, including those in developing countries. These two facets combined can democratise AI knowledge and technology, fostering a more universally ethical and useful progress in AI.

 

What 2 people do you admire most on the earth of AI by way of their work?
  

I find Clément Delangue and the Huggingface founders admirable – notably their commitment to developing AI in an open and community-driven manner, which I consider may also support the points made within the previous query. And I find Mustafa Suleyman from Inflection impressive too for his commitment to secure AI development.

 

What advice would you give to aspiring AI researchers and enthusiasts who have the desire to make a positive impact in the sector?
  

There’s three key points. Strive for interdisciplinary, collaborative and community-driven projects – ones that prioritise the human being on the centre of each AI development and AI use. Consider ethics as the primary priority. And construct things that make humans higher – things that should not intended to switch people.

 

If you might solve any global problem on the earth with AI, what wouldn’t it be and why?

 

I did my PhD within the genetics of bacteria, and developing tools to analyse the DNA of bacteria to detect drug resistance and solve the issue of anti-microbial resistance. I’m going to be watching AI’s application in biology and genetics with interest. There’s some pretty meaty problems there that may very well be solved with AI use. Although Large Language Models are focused on human language, the DNA behind genetics is a language too.

What inspired you to take part in this AI summit as a speaker, and what message do you hope to convey to the audience?
  

The very heart of my talk, and why we decided to come back to World Summit AI, is that everyone seems to be aware of the proven fact that there may be quite a lot of data powering the event of AI tools. Therefore increasingly, there may be the necessity for scientifically valid data sets. Human intelligence is at the guts of developing Artificial Intelligence, so my message to the summit is that the people behind the information matter. Let’s construct AI together – in an inclusive, collaborative and open manner.

 

 

Global AI events calendar

 

11-12 October 2023

Amsterdam, Netherlands

 

World AI Week 

9-13 October 2023

Amsterdam, Netherlands

 

24-25 April 2024
Montréal, Canada

 

Intelligent Health

11-12 September 2024

Basel, Switzerland

 

 

 

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This article was originally published at blog.worldsummit.ai