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To give AI - focused women academics and others their well - deserved — and overdue — time in the spotlight , TechCrunch has been publishinga series of interviewsfocusing on noteworthy cleaning lady who ’ve contributed to the AI revolution .
Raji Arasu , the CTO of Autodesk , said that she ’s been using AI for “ multiple decades ” to solve software - relate challenges .
“ When traditional software development approaches — whether deterministic , adjective , or other conventional methods — have struggled to address complex problems , I ’ve always turned to AI as a resolution , particularly for handling amorphous data , ” Arasu told TechCrunch in an interview .
Arasu has had a long career in tech . In 1993 , she join Oracle as an engineering science technical lead . A few years later , she was hired as a senior manager at eBay , where she worked her way up to the office of VP of technology . After tenures at StubHub and Intuit , Arasu accepted an offering at Autodesk , where she ’s remained for the retiring four years .
Many of these opportunity were made possible by a unassailable professional support net , Arasu suppose . She ’s tried to facilitate others succeed in crook by becoming vocal about supporting womanhood in technical school and driving DEI opening , especially for boards of directors .
“ ahead of time in my career , I was fortunate to have a various set of mentors who played a all important role in form my confidence , boldly voicing my linear perspective and opinions , advocating for myself , and assure I had a seat at the table , ” Arasu pronounce . “ insure that woman , mass of color , and person with diverse backgrounds are part of strategic decisiveness - making is life-sustaining . ”
In her various jobs throughout the years , Arasu said that she ’s had a front - row prat to exciting exploitation in the AI place . For model , at Autodesk , she ’s overseen the company ’s R&D org , which has explored AI applications in area like mental synthesis automation , 3D modeling , and applied science design .
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“ Advances in cypher power , generative AI , and the domain - specific mulct - tuning of turgid models have shatter the boundaries of enquiry laboratory and take these technologies into the real world , ” she said . “ I ’m charm by the fastness at which this AI gyration is poised to transform industries across the board . ”
While exciting , AI come with peril , Arasu acknowledged . AI systems often speculate the preconception and assumption of their creators , she said , and there are significant concerns around data point privacy , foil , and data practices that need to be plow .
Arasu is a advocate of “ close collaboration ” between manufacture and governance to make “ well - informed ” AI policy that start to address these risks . But she cautioned that strong policy can only emerge from multidisciplinary and divers collaborative groups .
“ Unlike past revolution , there is now a clear recognition that inclusive involvement — especially by woman — is of the essence for achieving fairer , more innovative outcome in AI and other emerging technologies , ” Arasu said . “ As AI reshapes industries like health care , didactics , and finance , women ’s involvement ensure the technology reflects a encompassing grasp of human experience . ”
self-governing of regulation , when it comes to build AI responsibly , Arasu believes companies must ensure that their system are clear about how they function , so that user can confide — or at least not distrustfulness — decisions they make . This start , she articulate , with understand customers ’ business organization about their data point , putting practices in place to protect proprietary and personal information , and maintaining compliance with global policies .
“ honorable blueprint should guide the evolution process , ” Arasu say , “ incorporate diverse linear perspective and creating governance frameworks to reduce bias and manage jeopardy . uninterrupted monitoring and accountability are essential , secure that AI system work as intended , and cover issues promptly . ultimately , nurture crabby - disciplinal collaboration centered around the customer help oneself ensure AI serve the vulgar good , navigating risks and maximizing its benefits for society . ”
