A Pienso, fundada por ex-alunos, desenvolveu um construtor de IA fácil de usar para que especialistas em domínio possam construir soluções sem escrever nenhum código.
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Pienso desenvolveu um construtor de IA sem código para que as pessoas mais próximas dos problemas possam usar a tecnologia em vez de depender de engenheiros de aprendizado de máquina. Imagem: Cortesia de Pienso
Como alunos do Media Lab em 2010, Karthik Dinakar SM '12, PhD '17 e Birago Jones SM '12 se uniram em um projeto de classe para construir uma ferramenta que ajudaria equipes de moderação de conteúdo em empresas como Twitter (agora X) e YouTube. O projeto gerou grande entusiasmo, e os pesquisadores foram convidados a fazer uma demonstração em uma cúpula sobre cyberbullying na Casa Branca – eles só precisavam fazer a coisa funcionar.
Um dia antes do evento na Casa Branca, Dinakar passou horas tentando montar uma demonstração funcional que pudesse identificar postagens preocupantes no Twitter. Por volta das 23h, ele ligou para Jones para dizer que estava desistindo.
Então Jones decidiu analisar os dados. Acontece que o modelo de Dinakar estava sinalizando os tipos certos de postagens, mas os postadores usavam gírias adolescentes e outras linguagens indiretas que Dinakar não percebeu. O problema não era o modelo; era a desconexão entre Dinakar e os adolescentes que ele tentava ajudar.
“Percebemos então, pouco antes de chegarmos à Casa Branca, que as pessoas que constroem esses modelos não deveriam ser apenas engenheiros de aprendizado de máquina”, diz Dinakar. “Devem ser pessoas que melhor entendam seus dados.”
O insight levou os pesquisadores a desenvolver ferramentas de apontar e clicar que permitem que não especialistas construam modelos de aprendizado de máquina. Essas ferramentas tornaram-se a base do Pienso, que hoje ajuda as pessoas a construir grandes modelos de linguagem para detectar desinformação, tráfico de seres humanos, venda de armas e muito mais, sem escrever qualquer código.
“Esses tipos de aplicativos são importantes para nós porque nossas raízes estão no cyberbullying e na compreensão de como usar a IA para coisas que realmente ajudam a humanidade”, diz Jones.
Quanto à versão inicial do sistema mostrada na Casa Branca, os fundadores acabaram colaborando com estudantes de escolas próximas em Cambridge, Massachusetts, para deixá-los treinar os modelos.
“Os modelos que essas crianças treinaram eram muito melhores e cheios de nuances do que qualquer coisa que eu pudesse imaginar”, diz Dinakar. “Birago e eu tivemos um grande 'Aha!' momento em que percebemos que capacitar especialistas de domínio – o que é diferente de democratizar a IA – era o melhor caminho a seguir.”
Um projeto com propósito
Jones e Dinakar se conheceram quando eram estudantes de pós-graduação no grupo de pesquisa de Agentes de Software do MIT Media Lab. O trabalho deles no que se tornou o Pienso começou no Curso 6.864 (Processamento de Linguagem Natural) e continuou até obterem o mestrado em 2012.
Acontece que 2010 não foi a última vez que os fundadores foram convidados à Casa Branca para demonstrar seu projeto. O trabalho gerou muito entusiasmo, mas os fundadores trabalharam em tempo parcial no Pienso até 2016, quando Dinakar terminou seu doutorado no MIT e o aprendizado profundo começou a explodir em popularidade.
“Ainda estamos conectados com muitas pessoas no campus”, diz Dinakar. “A exposição que tivemos no MIT, a fusão de interfaces humanas e de computador, ampliou nossa compreensão. Nossa filosofia na Pienso não seria possível sem a vibração do campus do MIT.”
Os fundadores também dão crédito ao Programa de Ligação Industrial (ILP) e ao Acelerador de Startup (STEX) do MIT por conectá-los aos primeiros parceiros.
Um dos primeiros parceiros foi SkyUK. A equipe de sucesso do cliente da empresa usou o Pienso para construir modelos para entender os problemas mais comuns de seus clientes. Hoje, esses modelos estão ajudando a processar meio milhão de chamadas de clientes por dia, e os fundadores dizem que economizaram para a empresa mais de £ 7 milhões de libras ao reduzir a duração das chamadas para o call center da empresa.
“ A diferença entre democratizar a IA e capacitar as pessoas com IA depende de quem entende melhor os dados: você, um médico, um jornalista ou alguém que trabalha com clientes todos os dias?” Jones diz. “Essas são as pessoas que deveriam criar os modelos. É assim que você obtém insights de seus dados.”
Em 2020, logo quando os surtos de Covid-19 começaram nos EUA, funcionários do governo contactaram os fundadores para utilizarem a sua ferramenta para compreender melhor a doença emergente. Pienso ajudou especialistas em virologia e doenças infecciosas a criar modelos de aprendizado de máquina para explorar milhares de artigos de pesquisa sobre coronavírus. Dinakar diz que mais tarde souberam que o trabalho ajudou o governo a identificar e fortalecer cadeias de abastecimento críticas de medicamentos, incluindo o popular antiviral remdesivir.
“Esses compostos foram descobertos por uma equipe que não conhecia o aprendizado profundo, mas foi capaz de usar nossa plataforma”, diz Dinakar.
Construindo um futuro melhor para a IA
Como o Pienso pode ser executado em servidores internos e infraestrutura em nuvem, os fundadores dizem que ele oferece uma alternativa para as empresas serem forçadas a doar seus dados usando serviços oferecidos por outras empresas de IA.
“A interface do Pienso é uma série de aplicativos da web costurados”, explica Dinakar. “Você pode pensar nisso como um Adobe Photoshop para modelos de linguagem grandes, mas na web. Você pode apontar e importar dados sem escrever uma linha de código. Você pode refinar os dados, prepará-los para aprendizado profundo, analisá-los, dar-lhes estrutura se não estiverem rotulados ou anotados, e você pode obter um modelo de linguagem grande e ajustado em questão de 25 minutos.”
No início deste ano, a Pienso anunciou uma parceria com a GraphCore, que fornece uma plataforma de computação mais rápida e eficiente para aprendizado de máquina. Os fundadores dizem que a parceria reduzirá ainda mais as barreiras ao aproveitamento da IA, reduzindo drasticamente a latência.
“Se você estiver construindo uma plataforma interativa de IA, os usuários não tomarão uma xícara de café toda vez que clicarem em um botão”, diz Dinakar. “Precisa ser rápido e responsivo.”
Os fundadores acreditam que a sua solução está a permitir um futuro onde modelos de IA mais eficazes serão desenvolvidos para casos de utilização específicos pelas pessoas que estão mais familiarizadas com os problemas que estão a tentar resolver.
“Nenhum modelo pode fazer tudo”, diz Dinakar. “A aplicação de cada pessoa é diferente, as suas necessidades são diferentes, os seus dados são diferentes. É altamente improvável que um modelo faça tudo por você. Trata-se de reunir um jardim de modelos e permitir que colaborem entre si e orquestrá-los de uma forma que faça sentido – e as pessoas que fazem essa orquestração devem ser as que melhor entendem os dados.”