Collaborating Authors

artificial intelligence

Prima-Temp Awarded Groundbreaking Patent Using Artificial Intelligence For Ovulation Prediction


10,828,015, issued November 10, 2020, describes technology that applies AI to ovulation prediction. The patent is part of an intellectual property …

When AI Systems Fail: Introducing the AI Incident Database - The Partnership on AI


Governments, corporations, and individuals are increasingly deploying intelligent systems to safety-critical problem areas, such as transportation, energy, health care, and law enforcement, as well as challenging social system domains such as recruiting. Failures of these systems pose serious risks to life and wellbeing, but even well-intentioned intelligent system developers fail to imagine what can go wrong when their systems are deployed in the real world. These failures can lead to dire consequences, some of which we've already witnessed, from a trading algorithm causing a market "flash crash" in 2010 to an autonomous car killing a pedestrian in 2018 and a facial recognition system causing the wrongful arrest of an innocent person in 2019. Worse, the artificial intelligence community has no formal systems or processes whereby practitioners can discover and learn from the mistakes of the past, especially since there is not a widely used centralized place to collect information about what has gone wrong previously. Avoiding repeated AI failures requires making past failures known.

COVID-19 Update on Global Artificial Intelligence (AI) Chips Market Analysis 2020-26 By AMD …


The Artificial Intelligence (AI) Chips industry research document also delivers a brief analysis of several definitions, applications, geographical …

Artificial Intelligence Against Corruption


Corruption grows when accountability is low -- it is hard to imagine a politician abusing their power for personal gain if they knew for certain that they would get caught and punished. This is why improving accountability is a wining strategy for fighting corruption, and Artificial Intelligence technology can help us do that. Whether we realize it or not, AI technologies that spot wrongdoing are already all around us. Credit card companies, for example, have been using it for years -- if your card is used in strange countries, to buy strange products, in a price range that is strange to your normal behavior, the company's AI models are likely to flag it as suspicious. And it does so incredibly fast for millions and millions of transactions everyday.

How Should We Think about the Ethics of Artificial Intelligence?


FM: New and emerging tech products are now embedded in almost every industry, so the ethical challenges of technologies like AI aren't limited to the …

Role Of AI And Machine Learning In Logistics Industry


We see rapid technological development in the fields of big data, algorithmic development, connectivity, cloud computing and processing power every day. These new technologies have made the performance, accessibility, and costs of AI more favourable than ever before. The introduction of modern and new technologies such as artificial intelligence, machine learning and blockchain has transformed the unorganised and fragmented logistics sector. These technologies bring modifications in logistics industries such as predictive analytics, autonomous vehicles, and smart roads. Artificial intelligence and Machine Learning are capturing more and more industries in every sector and spheres of our lives and logistics is not an exception.

#12 PotentiaMetrics: Bobby Palmer on data & AI for personalized treatment plans


On this episode of Brains Behind AI, Ari and Natalie met with Bobby Palmer, the President and CEO of PotentiaMetrics, an Austin-based healthcare data and AI company. PotentiaMetrics' data analytics and artificial intelligence platforms help providers, payers and medical technology companies inform personalized treatment plans by comparing patient-level outcome data related to survival, quality of life and cost of care. These companies use PotentiaMetrics platforms to compare effectiveness, adjust for risk, and track outcomes-based performance metrics. Bobby has been a business owner and CEO for over 20 years, creating the vision and strategic direction to develop multi-institutional, real-world outcomes registries that enable the creation of unique and personalized AI platforms. Bobby received his MBA from Washington University in St. Louis.

A definitive explanation to Hinge Loss for Support Vector Machines.


NOTE: This article assumes that you are familiar with how an SVM operates. If this is not the case for you, be sure to check my out previous article which breaks down the SVM algorithm from first principles, and also includes a coded implementation of the algorithm from scratch! I have seen lots of articles and blog posts on the Hinge Loss and how it works. However, I find most of them to be quite vague and not giving a clear explanation of what exactly the function does and what it is. Instead, most of the time an unclear graph is shown and the reader is left bewildered.

Building Fair Machine Learning: Interview the Co-Founders of Fairly AI


David Van Bruwaene and Fion Lee-Madan are the co-founders of Fairly AI, a Waterloo-Toronto Founder Institute portfolio company. Fairly AI is a tool for organizations to audit their artificial intelligence (AI) systems from across all business units, to eliminate bias, protect privacy, and ensure transparency of automated decisions. Research into fairness in machine learning is a topic becoming of increasingly greater interest to laypeople and non-technologists, because the implications that "biased" artificial intelligence can have on society are enormous. For example, if industries like housing, lending, education, or human resources that utilize AI in their decision-making - based on historical data that included variables such as gender, ethnicity, or disability - the AI may learn to replicate that input data's statistical regularities. If there was a pattern of discrimination in the "input data," then there will likely be a discriminatory pattern in the "output" data, resulting in machine learning that is not'fair.'