"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
In just the last two years, artificial intelligence has become embedded in scores of medical devices that offer advice to ER doctors, cardiologists, oncologists, and countless other health care providers. The Food and Drug Administration has approved at least 130 AI-powered medical devices, half of them in the last year alone, and the numbers are certain to surge far higher in the next few years. Several AI devices aim at spotting and alerting doctors to suspected blood clots in the lungs. Some analyze mammograms and ultrasound images for signs of breast cancer, while others examine brain scans for signs of hemorrhage. Cardiac AI devices can now flag a wide range of hidden heart problems.
All neural networks are susceptible to "adversarial attacks," where an attacker provides an example intended to fool the neural network. Any system that uses a neural network can be exploited. Luckily, there are known techniques that can mitigate or even prevent adversarial attacks completely. The field of adversarial machine learning is growing rapidly as companies realize the dangers of adversarial attacks. We will look at a brief case study of face recognition systems and their potential vulnerabilities.
According to reports, machine learning is one of the most sought-after jobs in 2021. Companies have been in high demand for machine learning engineers to build algorithms that can enable business growth and efficiency. Disruptive technology is not a stranger anymore. Companies are pouring money into the development and deployment of cutting-edge technologies and automation. Companies are adopting business intelligence and automation to boost their services and get a deeper insight into the business.
Classical ERM () minimizes the average loss and is shown in pink. As (blue), TERM finds a line of best fit while ignoring outliers. In some applications, these'outliers' may correspond to minority samples that should not be ignored. As (red), TERM recovers the min-max solution, which minimizes the worst loss. This can ensure the model is a reasonable fit for all samples, reducing unfairness related to representation disparity.
Does this concept of VC Dimension carry over to models in time series analysis? Is it possible to show that LSTM's have a higher VC dimension compared to ARIMA style models? Supposedly, neural network based time series models were developed because modeols like ARIMA was unable to provide reliable estimates for bigger and complex datasets. Mathematically speaking, what allows a LSTM to capture more variation and complexity in a dataset compared to ARIMA? Just as a general question: in what instances would it be better to use a CNN for time series forecasting compared to an LSTM?
"Machine intelligence is the last invention that humanity will ever need to make". The quote definitely makes it clear that machine learning is the future and vast opportunities and benefits for all. Let this be a fresh start for you to learn a really interesting algorithm in machine learning. As you all know, we often come across the problems of storing and processing huge data in machine learning tasks, as it's a time-consuming process and difficulties to interpret also arises. Not every feature of the data is necessary for predictions.
It's "misleading and counterproductive" to block the use of machine-learning algorithms in the justice system on the grounds that some of them may be subject to racial bias, according to a forthcoming study in the American Criminal Law Review. The use of artificial intelligence by judges, prosecutors, police and other justice authorities remains "the best means to overcome the pervasive bias and discrimination that exists in all parts of the deeply flawed criminal justice system," said the study. Algorithmic systems are used in a variety of ways in the U.S. justice system in practices ranging from identifying and predicting crime "hot spots" to real-time surveillance. More than 60 kinds of risk assessment tools are currently in use by court systems around the country, usually to weigh whether individuals should be held in detention before trial or can be released on their own recognizance. The risk assessment tools, which assign weights to data points such as previous arrests and the age of the offender, have come under fire from activists, judges, prosecutors, and some criminologists who say they are susceptible to bias themselves.
With this publication, we launch a new column for AI Magazine on the role of open-source software in artificial intelligence. As the column editor, I would like to extend my welcome and invite AI Magazine readers to send short articles for future columns, which may appear in the traditional print version of AI Magazine, or on the AI Magazine interactive site currently under development. This introductory column serves to highlight my interests in open-source software and to propose a few topics for future columns. The field of artificial intelligence (AI) is arguably 64 years old now, as measured from the summer of 1956 Dartmouth Workshop. What is most surprising to you about AI today?
Registered reports have been proposed as a way to move from eye-catching and surprising results and toward methodologically sound practices and interesting research questions. However, none of the top-twenty artificial intelligence journals support registered reports, and no traces of registered reports can be found in the field of artificial intelligence. Is this because they do not provide value for the type of research that is conducted in the field of artificial intelligence? Registered reports have been touted as one of the solutions to the problems surrounding the reproducibility crisis. They promote good research practices and combat data dredging1.
For years we've been told that data science is the future; that artificial intelligence (AI) and machine learning (ML) will enable us to automate everything. And yet most (85%) data science projects fail, according to a 2019 report, though such scare statistics might not reflect reality. Still, there are plenty of reasons why a data science project might not work as advertised, but one reason stands out: Talent. Or, rather, the lack thereof, as Gartner has highlighted. If you're thinking, "Well, I'll just send my recruiters to LinkedIn to scour for talent," I have news for you: It's not going to work.