The decline in trust underlines the importance for companies to own their narrative and be transparent about the benefits as well as possible downsides of technologies. The benefits of a trusted business are crucial and can truly determine not only whether your product or service takes off but also its credibility. Why should this be a concern for tech investors? According to the latest Edelman Trust Barometer 2021, trust in technology has declined. But what about investors? Shouldn’t investors also be analyzing the ethical AI risk of their portfolio companies? If companies need to understand their exposure both in the business-to-business and business-to-consumer contexts then so too should investors who have invested in a business. It goes without question that business leaders hoping to shift their posture from hindsight to foresight need to better understand the types of risks they are taking on, their interdependencies, and their underlying causes.
The business leaders of the future who purchase, build and deploy such systems are likely to face costly repercussions, both financial and reputational. Implementing technologies without regard for their impact is risky and increasingly unacceptable to wider society. Of course we’re not suggesting that Nabla would have released the GPT-3 chatbot but the experiment shows that defects in these foundation models can be inherited by all the adapted models downstream carrying with it significant risks. The results were less than ideal suggesting suicide was a good idea and to recycle to relieve stress. That’s exactly what French start-up, Nabla, tried to do with an experiment using GPT-3 to build a medical chatbot. The scale and adaptiveness of these systems could turn them into ‘foundation models’ with new start-ups utilising these systems to build out specific applications in health, law, finance etc.
#Gpt 3 chatbot full#
These systems can turn their hand to many different tasks and whose full capabilities can no longer be predicted with absolute certainty.
The sheer scale of these systems have opened the door to more generalised artificial intelligence that can be applied to a number of different tasks. Over the past 2 years researchers have built ever bigger, more powerful AI systems (e.g.
The AI world is on the cusp of a paradigm shift.
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