This is the second'I, Lawyer' podcast Artificial Lawyer/TromansConsulting has done with Sweden's leading legal tech writer, Fredrik Svärd, who runs the super-informative, Legaltech.se In this approximately 30 minutes chat we knock around a few subjects, such as where legal AI as an industry has got to; how the use of algorithms does not always mean there is any AI involved; why AI may be the answer to removing bias rather than the cause of it, and much, much more. We also give a special shout out to Lexpo, which is now just around the corner and will take place in Amsterdam 8 9 May 2017. Many thanks to Fred for organising and producing the podcast, which is below on Soundcloud.
Use this easy-to-understand, downloadable infographic overview of machine learning basics to identify the popular algorithms used to answer common machine learning questions. Algorithm examples help the machine learning beginner understand which algorithms to use and what they are used for. Azure Machine Learning Studio comes with a large number of machine learning algorithms that you can use to solve predictive analytics problems. The downloadable infographic below demonstrates how the four types of machine learning algorithms - regression, anomaly detection, clustering, and classification - can be used to answer your machine learning questions. Get the most out of the infographic by downloading it - the PDF has links to examples of each algorithm.
In recent years, artificial intelligence has struggled with a major PR problem: whether or not it's intentional, developers keep programming biases into their systems, creating algorithms that reflect the same prejudiced perspectives common in society. That's why it's intriguing that engineers from MIT and Harvard University say they've developed an algorithm that can scrub the bias from AI -- like sensitivity training for algorithms. The tool audits algorithms for biases and helps re-train them to behave more equitably, according to new research presented this week at the Conference on Artificial Intelligence, Ethics and Society. And even then, once complex AI systems deploy in the real world, it becomes very difficult to evaluate how exactly they're making their decisions. That's why automating the process is so important -- the new tool can go in and reconfigure how much value the AI system gives to each aspect of its training data, according to the research.
Nowadays, artificial intelligence (AI) is ubiquitous. We can hardly open a newspaper or tune in to a news show without getting some story about AI. AI is probably the technology most talked about. But AI means different things to different people. I've been working in the field of AI, both in industry and academia, since the late 80's.