If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
The American Physical Society (APS) has recognized a summer intern at the U.S. Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL) for producing an outstanding research poster at the world-wide APS Division of Plasma Physics (DPP) gathering last October. The student, Marco Miller, a senior at Columbia University majoring in applied physics, used machine learning to accelerate a leading PPPL computer code known as XGC as a participant in the DOE's Summer Undergraduate Laboratory Internship (SULI) program in 2019. The modifications, which will enable the XGC code to calculate more quickly, could help expand the physics included in detailed simulations of the plasma that fuels fusion reactions. The poster, prepared under the mentorship of PPPL physicist Michael Churchill, showed how Miller used machine learning techniques in his research and was presented at the APS-DPP conference in Fort Lauderdale, Florida. "It felt great to get the award," Miller said.
This 20-hour Machine Learning with Python course covers all the basic machine learning methods and Python modules (especially Scikit-Learn) for implementing them. The five sessions cover: simple and multiple Linear regressions; classification methods including logistic regression, discriminant analysis and naive bayes, support vector machines (SVMs) and tree based methods; cross-validation and feature selection; regularization; principal component analysis (PCA) and clustering algorithms. After successfully completing of this course, you will be able to explain the principles of machine learning algorithms and implement these methods to analyze complex datasets and make predictions in Python.
The original GAN paper by Goodfellow et al did not work for discrete data because of the inability to the backproagate gradient to the generator. Have there been any recent developments in this directions since, allowing the adversarial idea to be implemented for discrete data? And still take advantage of the automatic differentiation (backpropagation) facilities of libraries like PyTorch / Tensorflow?
We are in the midst of an unprecedented surge of interest in machine learning (ML) and artificial intelligence (AI) technologies. These tools, which allow computers to make data-derived predictions and automate decisions, have become part of daily life for billions of people. Ubiquitous digital services such as interactive maps, tailored advertisements, and voice-activated personal assistants are likely only the beginning. Some AI advocates even claim that AI's impact will be as profound as "electricity or fire" that it will revolutionize nearly every field of human activity. This enthusiasm has reached international development as well.
You've spent months studying data science, now it's time to find a job in the industry. Fortunately, companies all over the world are looking to hire data scientists -- and fast. According to LinkedIn's 2020 U.S. Emerging Jobs Report, skills related to Machine Learning, Deep Learning, TensorFlow, Python, Natural Language Processing, etc. seen more than 70% annual growth. According to an IBM survey, the openings for data and analytics talent in the US will continue to increase, reaching 133% growth in 2020, and creating more than 700,000 openings. Qualified candidates will have a multitude of vacancies to choose from when ready to seek out a new position in the field.
Casino executives, industry analysts and lawyers attended a conference at the UNLV Boyd School of Law to consult on how biometrics, AI and machine learning could shape the future of Las Vegas casinos, writes the Nevada Independent. While there are many opportunities for the gaming industry, most machine learning and facial recognition-enabled product ideas addressed customer service and customer recognition. These include slot machines that leverage facial biometrics to recognize important or banned players, and reduce fraud attempts, or facial recognition-equipped tables to help pit managers identify and track known players. "What we're seeing is this introduction of technology into the gaming industry in ways we've never seen before, and because of it, it started to raise issues -- or questions -- as to how this works and what the ramifications could be for things like patron privacy, anonymity and data protection," said Anthony Cabot, Distinguished Fellow in Gaming Law at the UNLV Boyd School of Law and event organizer. While speakers focused on presentations about competing laws and technology problems, there was not enough discussion on how to solve these problems, according to the report, yet Cabot hopes the gaming industry and regulators will join forces to deliver solutions.
In a usual management setting, after a person has had a heart attack or stroke, algorithmic risk models are used to calculate the risk of death for the patient. These algorithms or models utilize various factors such as age of the patient, gender, previous history, family history, ethnicity etc. Treatment of the patient is often guided by these models. A new study has shown that in many cases these models fail to predict the risks accurately. This may lead to treatment choices that are unnecessary or ineffective and even risky for the patients. The new study was published in the Digital Medicine.
We are entering a new decade that is defined by data. Organizations will either succeed or fail because of the way they collect, use, and democratize data analytics across their organization. At this crucial turning point in business transformation, companies must embrace change and invest in it. In the recently released 10 Enterprise Analytics Trends for 2020, MicroStrategy consulted leading industry experts to identify the key trends that will impact the analysis of corporate data for 2020 and beyond. Three key themes were identified: the crucial role of artificial intelligence (AI), the focus on digital skills and the growth of data.
In this talk, we will take an in-depth look at various mechanisms of attack detection, from signatures and regular expressions to machine learning. Attack detection is critical for most security solutions, whether we are talking about a load balancer-based (NIDS, WAF), host-based or in-application solutions (HIDS, RASP). Interestingly, regardless of the differences in architecture and data flow, most solutions use similar detection principles and techniques. We will explore how the detection architecture evolved over time and how the new generation of detection logic, such as the architecture implemented by some of the advanced application security tools, are principally different from that of the legacy solutions.