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action or later. Please see Debugging in WordPress for more information. (This message was added in version 6.7.0.) in /home4/scienrds/scienceandnerds/wp-includes/functions.php on line 6114Source:https:\/\/techcrunch.com\/2023\/06\/23\/when-ai-bots-pose-as-humans\/<\/a><\/br> Welcome to Startups Weekly. Sign up <\/em>here<\/em><\/a> to get it in your inbox every Friday afternoon.<\/em><\/p>\n This week, I\u2019ve been doing a lot of thinking about the implications of artificial intelligence. One of the most fragile parts of the puzzle is the training data. We already know that you can see if your images were used to train the datasets<\/a> and that a lot of the training datasets out there are . . . spurious at best. Some startups are trying to build datasets trained exclusively on licensed data<\/a>, and human artists are pretty grumpy when big-name studios use AI to generate art<\/a>.<\/p>\n An interesting curveball is realizing that even evaluating training data may be challenging, as researchers discover that Mechanical Turk workers \u2014 who are, in theory, human workers doing tasks that machines can\u2019t do \u2014 are reportedly using AI tools themselves. That\u2019s fine for some tasks, but not great if the text they are generating is meant to be used as the rubric that AI-generating text tools are measured against. The old computing adage of \u201cgarbage in, garbage out\u201d still stands. If you can\u2019t trust the training data, you can\u2019t trust the output<\/a> (TC+).<\/p>\n Seen through the lens of startups, AI is continuing to go gangbusters \u2014 and Amazon\u2019s AWS is throwing its not inconsiderable weight behind the burgeoning trend, with a $100 million program <\/a>to fund generative AI initiatives.<\/p>\n
\nWhen AI bots pose as humans<\/br>
\n2023-06-23 21:38:03<\/br><\/p>\nThe highest highs and deepest lows of transportation<\/h2>\n