Artificial intelligence definition
Several works use AI to force us to confront the fundamental question of what makes us human, showing us artificial beings that have the ability to feel, and thus to suffer https://newzcenter.com/transforming-sales-with-ai/. This appears in Karel Čapek’s R.U.R., the films A.I. Artificial Intelligence and Ex Machina, as well as the novel Do Androids Dream of Electric Sheep?, by Philip K. Dick. Dick considers the idea that our understanding of human subjectivity is altered by technology created with artificial intelligence.
Deep learning began to dominate industry benchmarks in 2012 and was adopted throughout the field. For many specific tasks, other methods were abandoned. Deep learning’s success was based on both hardware improvements (faster computers, graphics processing units, cloud computing ) and access to large amounts of data (including curated datasets, such as ImageNet). Deep learning’s success led to an enormous increase in interest and funding in AI. The amount of machine learning research (measured by total publications) increased by 50% in the years 2015–2019.
Natural language processing (NLP) allows programs to read, write and communicate in human languages such as English. Specific problems include speech recognition, speech synthesis, machine translation, information extraction, information retrieval and question answering.
Finance is one of the fastest growing sectors where applied AI tools are being deployed: from retail online banking to investment advice and insurance, where automated “robot advisers” have been in use for some years.
Artificial intelligence ai
The tech is also creating new questions about how we keep all kinds of data — even our thoughts — private. AI has made facial recognition and surveillance commonplace, causing many experts to advocate banning it altogether. At the same time that AI is heightening privacy and security concerns, the technology is also enabling companies to make strides in cybersecurity software.
Deep learning has profoundly improved the performance of programs in many important subfields of artificial intelligence, including computer vision, speech recognition, natural language processing, image classification, and others. The reason that deep learning performs so well in so many applications is not known as of 2023. The sudden success of deep learning in 2012–2015 did not occur because of some new discovery or theoretical breakthrough (deep neural networks and backpropagation had been described by many people, as far back as the 1950s) but because of two factors: the incredible increase in computer power (including the hundred-fold increase in speed by switching to GPUs) and the availability of vast amounts of training data, especially the giant curated datasets used for benchmark testing, such as ImageNet.
YouTube, Facebook and others use recommender systems to guide users to more content. These AI programs were given the goal of maximizing user engagement (that is, the only goal was to keep people watching). The AI learned that users tended to choose misinformation, conspiracy theories, and extreme partisan content, and, to keep them watching, the AI recommended more of it. Users also tended to watch more content on the same subject, so the AI led people into filter bubbles where they received multiple versions of the same misinformation. This convinced many users that the misinformation was true, and ultimately undermined trust in institutions, the media and the government. The AI program had correctly learned to maximize its goal, but the result was harmful to society. After the U.S. election in 2016, major technology companies took steps to mitigate the problem .
The tech is also creating new questions about how we keep all kinds of data — even our thoughts — private. AI has made facial recognition and surveillance commonplace, causing many experts to advocate banning it altogether. At the same time that AI is heightening privacy and security concerns, the technology is also enabling companies to make strides in cybersecurity software.
Deep learning has profoundly improved the performance of programs in many important subfields of artificial intelligence, including computer vision, speech recognition, natural language processing, image classification, and others. The reason that deep learning performs so well in so many applications is not known as of 2023. The sudden success of deep learning in 2012–2015 did not occur because of some new discovery or theoretical breakthrough (deep neural networks and backpropagation had been described by many people, as far back as the 1950s) but because of two factors: the incredible increase in computer power (including the hundred-fold increase in speed by switching to GPUs) and the availability of vast amounts of training data, especially the giant curated datasets used for benchmark testing, such as ImageNet.
YouTube, Facebook and others use recommender systems to guide users to more content. These AI programs were given the goal of maximizing user engagement (that is, the only goal was to keep people watching). The AI learned that users tended to choose misinformation, conspiracy theories, and extreme partisan content, and, to keep them watching, the AI recommended more of it. Users also tended to watch more content on the same subject, so the AI led people into filter bubbles where they received multiple versions of the same misinformation. This convinced many users that the misinformation was true, and ultimately undermined trust in institutions, the media and the government. The AI program had correctly learned to maximize its goal, but the result was harmful to society. After the U.S. election in 2016, major technology companies took steps to mitigate the problem .
Artificial intelligence in healthcare
In July 2020, it was reported that an AI algorithm developed by the University of Pittsburgh achieves the highest accuracy to date in identifying prostate cancer, with 98% sensitivity and 97% specificity. In 2023 a study reported the use of AI for CT-based radiomics classification at grading the aggressiveness of retroperitoneal sarcoma with 82% accuracy compared with 44% for lab analysis of biopsies.
First, radiologists do more than read and interpret images. Like other AI systems, radiology AI systems perform single tasks. Deep learning models in labs and startups are trained for specific image recognition tasks (such as nodule detection on chest computed tomography or hemorrhage on brain magnetic resonance imaging). However, thousands of such narrow detection tasks are necessary to fully identify all potential findings in medical images, and only a few of these can be done by AI today. Radiologists also consult with other physicians on diagnosis and treatment, treat diseases (for example providing local ablative therapies) and perform image-guided medical interventions such as cancer biopsies and vascular stents (interventional radiology), define the technical parameters of imaging examinations to be performed (tailored to the patient’s condition), relate findings from images to other medical records and test results, discuss procedures and results with patients, and many other activities.
Artificial intelligence can be, and in some wealthy countries is already being used to improve the speed and accuracy of diagnosis and screening for diseases; to assist with clinical care; strengthen health research and drug development, and support diverse public health interventions, such as disease surveillance, outbreak response, and health systems management.
Artificial intelligence stocks
Adobe develops and supports design and publishing software, which it sells on a subscription basis. The company’s Creative Cloud suite includes its flagship program Adobe Photoshop and other applications used by marketers, designers and students.
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Adobe produces creative content software and other applications used for marketing and e-commerce. The company’s Firefly generative machine learning model is generating customer interest across Photoshop, Illustrator and other platforms. Adobe has also applied its Sensei AI and machine learning technology to its Adobe Analytics, Campaign and Target products. Adobe recently launched new AI tools that allow customers to create images based on Adobe’s stock image library, while still compensating original artists. Bonner says Adobe’s rapid innovation and ongoing AI integration will help steadily improve its product offerings. Argus has a “buy” rating and $675 price target for ADBE stock, which closed at $547.93 on Dec. 9.
Meta Platforms is a market leader in social media and online advertising and is the parent company of Facebook, Instagram and other platforms. CEO Mark Zuckerberg has adopted a bold strategy of making Meta’s AI technology free and open to the public in an effort to gain market share and drive down competitors’ prices. In October, Meta released a batch of new AI models, including a “self-taught evaluator” that can be used to evaluate AI models. Bonner says Meta can increasingly monetize its AI user base over time. Argus has a “buy” rating and $660 price target for META stock, which closed at $613.57 on Dec. 9.
The stock began trading in 2020, so it doesn’t have a long track record. But it makes the list because it is up handsomely over the last year and analysts project 50.9% yearly EPS growth over the next five years. That is essentially reducing losses since the company isn’t profitable yet.