As an authority in the sector, what key challenges do you’re thinking that the AI community needs to handle to make sure responsible and ethical AI use?
The people behind your data are necessary. We need to make sure now we have a various workforce that gives the human intelligence that makes artificial intelligence higher. It is vital that we make sure that individuals are representative of the population if we would like to have accurate and unbiased AI. And also that we protect human dignity when developing these tools. It is vital that we develop AI systems that support people, not replace them. The goal needs to be to make people more efficient and productive. This is the important thing to developing a successful future.
How has AI impacted your specific field and what transformative changes do you expect to see within the near future?
There have been positive and tougher changes, and I'll start with the tougher ones. We have seen an increasing use of AI by participants in online research, leading to a discount in data quality. Essentially, individuals are using AI to enhance their very own answers in ways the researcher didn’t intend. On the positive side, there may be tremendous value in synthesizing research and understanding what has already been done in the sector. If you might be in a position to summarize and explain research results, you now not should be an authority to know scientific papers – you’ll be able to let AI explain them to you.
How do you envision AI shaping different industries and what advice would you give to corporations trying to integrate AI into their operations?
The most diverse use case for AI shall be to expand people's day by day uses – especially as a way of writing and communication. When it involves advice, first take a look at increasing people's productivity somewhat than viewing AI as a substitute for human intelligence. Understand the source of the information used to coach these tools and make sure that it’s sufficient to your use case. And provide clear guidelines for a way people should use AI – how much private data could be uploaded? What training clarity is there about how best to make use of these tools?
What opportunities and challenges do you’re thinking that AI offers for labor markets and human resources development worldwide?
Roles that leverage AI and human collaboration will increase. The earliest type of this may be prompt engineers, or people who find themselves experts at maximizing the outcomes of AI tools. Again, it can be crucial that it is a productivity increase and never a human substitute. In this context, helping people do their jobs effectively along with AI is crucial. In certain industries – and knowledge staff are particularly affected – there may be a must reskill employees and train them on learn how to use AI to do their jobs more effectively.
Can you give an example of an AI application or project that impressed you personally and explain why it’s so special?
Hume AI has been working on collecting speech and emotion data from people and developing a toolkit that understands human emotional expression. They recently did a really cool demo where they watched the song “Used To Be Young” by Miley Cyrus and depicted their emotional expressions throughout the video, predicting their pain, joy, fear, relief, etc. This is a robust example since it uses AI to know human behavior beyond just natural language processing.
What measures do you’re thinking that needs to be taken to shut the gap in AI research between developed and developing countries and ensure equitable technological progress?
To close the gap in AI research between developed and developing countries, two key strategies are critical: access to quality data and integrative research. First, equal access to datasets is important – open science brings more people into the community. Open source initiatives can democratize this access and empower researchers worldwide. Second, it’s crucial to advertise an inclusive research ecosystem by including participants and researchers from diverse and sometimes marginalized backgrounds, including from developing countries. These two features together can democratize AI knowledge and technology and promote generally more ethical and useful progress in AI.
Which two people do you admire most on the planet of AI when it comes to their work?
I find Clément Delangue and the Huggingface founders admirable – especially their commitment to the open and community-oriented development of AI, which I believe can even support the points raised within the previous query. And I also find Mustafa Suleyman from Inflection impressive 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 difference on this field?
There are three details. Aim for interdisciplinary, collaborative, and community-oriented projects—people who put humans at the guts of each AI development and AI use. Consider ethics as a top priority. And construct things that make people higher – things that are usually not designed to exchange people.
If you can solve one global problem on the planet with AI, what would it not be and why?
I did my PhD on the genetics of bacteria and developed tools to investigate the DNA of bacteria to detect drug resistance and solve the issue of antimicrobial resistance. I’ll follow the appliance of AI in biology and genetics with interest. There are some pretty serious problems that might be solved through the use of AI. Although Large Language Models give attention to human language, the DNA behind genetics can also be a language.
What inspired you to participate as a speaker at this AI Summit and what message would you prefer to convey to the audience?
The core of my talk and the explanation we decided to return to the World Summit AI is that everyone seems to be aware of the proven fact that there may be lots of data driving the event of AI tools. There is due to this fact an increasing need for scientifically valid data sets. Human intelligence is at the guts of the event of 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 way.
Global AI events calendar
Eleventh-Twelfth October 2023
Amsterdam, Netherlands
World AI Week
Ninth-Thirteenth October 2023
Amsterdam, Netherlands
Twenty fourth-Twenty fifth April 2024
Montreal Canada
Smart Health
Eleventh-Twelfth September 2024
Basel, Switzerland
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