• Artificial intelligence definition
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Artificial intelligence examples

Criticism of COMPAS highlighted that machine learning models are designed to make «predictions» that are only valid if we assume that the future will resemble the past ai for sales. If they are trained on data that includes the results of racist decisions in the past, machine learning models must predict that racist decisions will be made in the future. If an application then uses these predictions as recommendations, some of these «recommendations» will likely be racist. Thus, machine learning is not well suited to help make decisions in areas where there is hope that the future will be better than the past. It is descriptive rather than prescriptive.

There many other ways that AI is expected to help bad actors, some of which can not be foreseen. For example, machine-learning AI is able to design tens of thousands of toxic molecules in a matter of hours.

Active organizations in the AI open-source community include Hugging Face, Google, EleutherAI and Meta. Various AI models, such as Llama 2, Mistral or Stable Diffusion, have been made open-weight, meaning that their architecture and trained parameters (the «weights») are publicly available. Open-weight models can be freely fine-tuned, which allows companies to specialize them with their own data and for their own use-case. Open-weight models are useful for research and innovation but can also be misused. Since they can be fine-tuned, any built-in security measure, such as objecting to harmful requests, can be trained away until it becomes ineffective. Some researchers warn that future AI models may develop dangerous capabilities (such as the potential to drastically facilitate bioterrorism) and that once released on the Internet, they cannot be deleted everywhere if needed. They recommend pre-release audits and cost-benefit analyses.

Sensitive user data collected may include online activity records, geolocation data, video or audio. For example, in order to build speech recognition algorithms, Amazon has recorded millions of private conversations and allowed temporary workers to listen to and transcribe some of them. Opinions about this widespread surveillance range from those who see it as a necessary evil to those for whom it is clearly unethical and a violation of the right to privacy.

It is impossible to be certain that a program is operating correctly if no one knows how exactly it works. There have been many cases where a machine learning program passed rigorous tests, but nevertheless learned something different than what the programmers intended. For example, a system that could identify skin diseases better than medical professionals was found to actually have a strong tendency to classify images with a ruler as «cancerous», because pictures of malignancies typically include a ruler to show the scale. Another machine learning system designed to help effectively allocate medical resources was found to classify patients with asthma as being at «low risk» of dying from pneumonia. Having asthma is actually a severe risk factor, but since the patients having asthma would usually get much more medical care, they were relatively unlikely to die according to the training data. The correlation between asthma and low risk of dying from pneumonia was real, but misleading.

Artificial intelligence definition

AI gradually restored its reputation in the late 1990s and early 21st century by exploiting formal mathematical methods and by finding specific solutions to specific problems. This «narrow» and «formal» focus allowed researchers to produce verifiable results and collaborate with other fields (such as statistics, economics and mathematics). By 2000, solutions developed by AI researchers were being widely used, although in the 1990s they were rarely described as «artificial intelligence» (a tendency known as the AI effect). However, several academic researchers became concerned that AI was no longer pursuing its original goal of creating versatile, fully intelligent machines. Beginning around 2002, they founded the subfield of artificial general intelligence (or «AGI»), which had several well-funded institutions by the 2010s.

Generative AI is often trained on unlicensed copyrighted works, including in domains such as images or computer code; the output is then used under the rationale of «fair use». Experts disagree about how well and under what circumstances this rationale will hold up in courts of law; relevant factors may include «the purpose and character of the use of the copyrighted work» and «the effect upon the potential market for the copyrighted work». Website owners who do not wish to have their content scraped can indicate it in a «robots.txt» file. In 2023, leading authors (including John Grisham and Jonathan Franzen) sued AI companies for using their work to train generative AI. Another discussed approach is to envision a separate sui generis system of protection for creations generated by AI to ensure fair attribution and compensation for human authors.

AI gradually restored its reputation in the late 1990s and early 21st century by exploiting formal mathematical methods and by finding specific solutions to specific problems. This «narrow» and «formal» focus allowed researchers to produce verifiable results and collaborate with other fields (such as statistics, economics and mathematics). By 2000, solutions developed by AI researchers were being widely used, although in the 1990s they were rarely described as «artificial intelligence» (a tendency known as the AI effect). However, several academic researchers became concerned that AI was no longer pursuing its original goal of creating versatile, fully intelligent machines. Beginning around 2002, they founded the subfield of artificial general intelligence (or «AGI»), which had several well-funded institutions by the 2010s.

Generative AI is often trained on unlicensed copyrighted works, including in domains such as images or computer code; the output is then used under the rationale of «fair use». Experts disagree about how well and under what circumstances this rationale will hold up in courts of law; relevant factors may include «the purpose and character of the use of the copyrighted work» and «the effect upon the potential market for the copyrighted work». Website owners who do not wish to have their content scraped can indicate it in a «robots.txt» file. In 2023, leading authors (including John Grisham and Jonathan Franzen) sued AI companies for using their work to train generative AI. Another discussed approach is to envision a separate sui generis system of protection for creations generated by AI to ensure fair attribution and compensation for human authors.

Sensitive user data collected may include online activity records, geolocation data, video or audio. For example, in order to build speech recognition algorithms, Amazon has recorded millions of private conversations and allowed temporary workers to listen to and transcribe some of them. Opinions about this widespread surveillance range from those who see it as a necessary evil to those for whom it is clearly unethical and a violation of the right to privacy.

In May 2023, Geoffrey Hinton announced his resignation from Google in order to be able to «freely speak out about the risks of AI» without «considering how this impacts Google.» He notably mentioned risks of an AI takeover, and stressed that in order to avoid the worst outcomes, establishing safety guidelines will require cooperation among those competing in use of AI.

Artificial intelligence ai

As the future of AI unfolds, we can expect to see a continued acceleration of technological advancements, as well as the emergence of new ethical and societal considerations. By embracing the potential of AI while addressing its challenges, we can unlock new frontiers of innovation and progress that will shape the world of tomorrow.

Namun pada tahun 2024, sebagian besar peneliti dan praktisi AI—dan sebagian besar berita utama terkait AI—berfokus pada terobosan dalam AI generatif (gen AI), sebuah teknologi yang dapat menciptakan teks, gambar, video, dan konten orisinal lainnya. Untuk memahami AI generatif sepenuhnya, penting untuk terlebih dahulu memahami teknologi yang menjadi dasar pembuatan alat AI generatif: machine learning (ML) dan pembelajaran mendalam.

These risks can be mitigated, however, in a few ways. “Whenever you use a model,” says McKinsey partner Marie El Hoyek, “you need to be able to counter biases and instruct it not to use inappropriate or flawed sources, or things you don’t trust.” How? For one thing, it’s crucial to carefully select the initial data used to train these models to avoid including toxic or biased content. Next, rather than employing an off-the-shelf gen AI model, organizations could consider using smaller, specialized models. Organizations with more resources could also customize a general model based on their own data to fit their needs and minimize biases.

As the future of AI unfolds, we can expect to see a continued acceleration of technological advancements, as well as the emergence of new ethical and societal considerations. By embracing the potential of AI while addressing its challenges, we can unlock new frontiers of innovation and progress that will shape the world of tomorrow.

Namun pada tahun 2024, sebagian besar peneliti dan praktisi AI—dan sebagian besar berita utama terkait AI—berfokus pada terobosan dalam AI generatif (gen AI), sebuah teknologi yang dapat menciptakan teks, gambar, video, dan konten orisinal lainnya. Untuk memahami AI generatif sepenuhnya, penting untuk terlebih dahulu memahami teknologi yang menjadi dasar pembuatan alat AI generatif: machine learning (ML) dan pembelajaran mendalam.

These risks can be mitigated, however, in a few ways. “Whenever you use a model,” says McKinsey partner Marie El Hoyek, “you need to be able to counter biases and instruct it not to use inappropriate or flawed sources, or things you don’t trust.” How? For one thing, it’s crucial to carefully select the initial data used to train these models to avoid including toxic or biased content. Next, rather than employing an off-the-shelf gen AI model, organizations could consider using smaller, specialized models. Organizations with more resources could also customize a general model based on their own data to fit their needs and minimize biases.