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To giveAI - concentrate womenacademics and others their well - deserved — and overdue — time in the spotlight , TechCrunch has been publishing aseries of interviewsfocused on singular women who ’ve contribute to the AI gyration . We ’re publishing these pieces throughout the year as the AI thunder continues , highlighting key work that often pass unrecognized . Read more profileshere .
Catherine Breslin is the beginner and manager ofKingfisher Labs , where she helps companies develop AI strategy . She has spent more than two 10 as an AI scientist and has make for for Cambridge University , Toshiba Research , and even Amazon Alexa . She was antecedently an adviser to the VC monetary fund Deeptech Labs and was the Solutions Architect Director at Cobalt Speech & Language .
She look Oxford University for undergrad before receiving her sea captain ’s and Ph.D. at the University of Cambridge .
concisely , how did you get your start in AI ? What attract you to the field ?
I always love maths and physic at school and I select to canvas technology at university . That ’s where I first learned about AI , though it was n’t promise AI at the clip . I got scheme by the thought of using reckoner to do the language and language processing that we man discover leisurely . From there , I end up analyse for a PhD in voice applied science and work as a researcher . We ’re at a point in prison term where there ’ve been huge step forward for AI latterly , and I feel like there ’s a huge chance to build up technology that improves people ’s lives .
What body of work are you most proud of in the AI field ?
In 2020 , in the early days of the pandemic , I found my own consulting company with the missionary work to bring real - human beings AI expertness and leadership to organization . I ’m proud of the work I ’ve done with my clients across different and interesting projects and also that I ’ve been able to do this in a truly flexile agency around my kinsperson .
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How do you navigate the challenges of the male - dominated tech industriousness and , by denotation , the male - dominated AI industriousness ?
It ’s heavy to measure exactly , but something like 20 % of the AI field is woman . My sensing is also that the percentage gets low-down as you get more senior . For me , one of the best way to sail this is by establish a supportive electronic connection . Of naturally , support can come from people of any gender . Sometimes , though , it ’s reassuring to verbalize to cleaning lady who are facing similar site or who ’ve seen the same problems , and it ’s great not to palpate alone .
The other thing for me is to think carefully about where to spend my energy . I believe that we ’ll only see lasting change when more women get into senior and leadership positions , and that wo n’t happen if woman expend all their energy on fixing the system rather than supercharge their career . There ’s a pragmatic balance to be had between pushing for change and focusing on my own day-by-day work .
What advice would you give to women seeking to enter the AI field ?
AI is a Brobdingnagian and exciting bailiwick with a lot going on . There ’s also a huge amount of noise with what can seem like a perpetual stream of papers , products , and model being release . It ’s impossible to keep up with everything . Further , not every paper or enquiry resultant role is going to be significant in the long run , no matter how garish the press going . My advice is to find a niche that you ’re really concerned in attain progress in , memorize everything you may about that ecological niche , and harness the problems that you ’re prompt to solve . That ’ll give you the self-colored introduction that you ask .
What are some of the most urgent issue facing AI as it evolves ?
Progress in the past 15 years has been fast , and we ’ve see AI move out of the science lab and into products without really having maltreat back to in good order measure the berth and anticipate the consequence . One example that comes to mind is how much of our voice and language applied science performs better in English than other linguistic process . That ’s not to say that researchers have ignored other languages . Significant attempt has been put into non - English language applied science . Yet , the unintended consequence of better English language engineering science means that we ’re build and turn over out technology that does n’t serve well everyone equally .
What are some issues AI users should be aware of ?
I think people should be aware that AI is n’t a smooth-spoken bullet that ’ll resolve all problems in the next few year . It can be flying to build an impressive demo but take a lot of consecrate exploit to build up an AI system that consistently works well . We should n’t lose sight of the fact that AI is designed and build by human being , for world .
What is the good way to responsibly build AI ?
Responsibly build AI mean including various view from the start , including from your customers and anyone impacted by your product . exhaustively testing your organization is authoritative to be sure you know how well they work across a variety of scenarios . Testing gets the reputation of being boring work compared to the excitement of daydream up new algorithm . Yet , it ’s decisive to know if your product really works . Then there ’s the need to be honest with yourself and your customer about both the capability and limit of what you ’re building so that your organization does n’t get misused .
How can investor good push for responsible AI ?
startup are build up many new software of AI , and investors have a duty to be attentive about what they ’re choose to fund . I ’d love to see more investor be vocal about their visual modality for the hereafter that we ’re building and how creditworthy AI conform to in .
