AI News, Researchers were about to solve AI's black box problem, then the ... artificial intelligence

In theory-Sharing AI’s black box sounds great-But in reality it’s not

However, in reality those who seek to obtain that knowledge are not always so pure in motive – consider Nobel’s experience with dynamite (Patent # 78317) - something driven home at every meeting we present The Fleming Method for Tissue and Vascular Differentiation and Metabolism (FMTVDM) at [2], an example of which is shown in Figure 1.

During the Artificial Intelligence (AI) session at the same conference, one of the younger investigators in the audience asked IBM, Google, Apple and others-the panelists for the AI session - during the Q & A, why researchers should share their raw data with these corporations, who will then use the data to produce patented products.

We also agree with the release of non-IP data to appropriate journals and conferences, so the journals and conferences can determine research validity-all of which should occur prior to the conference or publication, to address any legitimate questions or concerns.

However, we do not agree with the release of IP data absent NDA and contractual agreements-and then believe such IP needs to be carefully protected from contamination and abuse [8] by individuals and corporations, whose motivations are not patient driven.

AI Isn’t a Solution to All Our Problems

From the esoteric worlds of predictive health care and cybersecurity to Google’s e-mail completion and translation apps, the impacts of AI are increasingly being felt in our everyday lived experience.

This software is built upon Google’s machine learning package TensorFlow—the same software that powers Google Translate, AirBnB’s house tagging, brain analysis for MRIs, education platforms, and more.

AI is also used in legal cases where it’s being employed to help legal advocates take on more cases because they need to spend less time on initial interviews with AI’s help.

globally employ people to perform repetitive classification tasks such as image recognition, creating the categorized data necessary to build an AI.

Beyond AI farms, online crowdsourcing projects are able to create robust tools because thousands of people come together to curate data.

With any new technological development, it is easy to wax poetic about the ways it can solve society’s ills—or hit every nail with your new hammer.

Amazon’s Rekognition AI falsely identified 28 sitting members of Congress as having been previously arrested, with people of color matched at twice the proportional rate of their representation.

The caucus continued: “This status quo results in an oversampling of data which, once used as inputs to an analytical framework leveraging artificial intelligence, could negatively impact outcomes in those oversampled communities”.

Applied this way, AI can only improve the productivity of specific resource-intensive dairy farms and is unlikely to meet Connecterra’s goal of ending world hunger.

The belief that AI is a cure-all tool that will magically deliver solutions if only you can collect enough data is misleading and ultimately dangerous as it prevents other effective solutions from being implemented earlier or even explored.Instead, we need to both build AI responsibly and understand where it can be reasonably applied.

This shields the user from understanding what biases and risks may be involved, and this lack of public understanding of AI tools and their limitations is a serious problem.

Makers should be able to communicate their data sources, why they chose those sources, how they’re trying to reduce bias in their programs, and how they’re providing user safeguards.

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