Learning algorithms are often used in conjunction with expert decision makers in practical scenarios, however this fact is largely ignored when designing these algorithms. In this paper we explore how to learn predictors that can either predict or choose to defer the decision to a downstream expert. Given only samples of the expert’s decisions, we give a procedure based on learning a classifier and a rejector and analyze it theoretically. Our approach is based on a novel reduction to cost sensitive learning where we give a consistent surrogate loss for cost sensitive learning that generalizes the cross entropy loss. We show the effectiveness of our approach on a variety of experimental tasks. Read More
Daily Archives: August 7, 2020
Facebook’s ‘Red Team’ Hacks Its Own AI Programs
Attackers increasingly try to confuse and bypass machine-learning systems. So the companies that deploy them are getting creative, spending heavily in recent years to deploy AI systems for tasks such as understanding the content of images or text.
… The work of protecting AI systems bears similarities to conventional computer security. Facebook’s AI red team gets its name from a term for exercises in which hackers working for an organization probe its defenses by role-playing as attackers. They know that any fixes they deploy may be side-stepped as their adversaries come up with new tricks and attacks. Read More
Conversational AI: Intelligent Virtual Assistants and the road ahead.
In a fast-moving world, customers require efficiency and promptness when talking to any company. Here is where chatbots and Intelligent Virtual Assistants (IVAs) come into play.
…According to Grand View Research, the global intelligent virtual assistant market size was valued at USD 3.7 billion in 2019, growing at a Compound Annual Growth Rate (CAGR) of 34.0% over the forecast period. Read More
Here’s why Apple believes it’s an AI leader—and why it says critics have it all wrong
Apple AI chief and ex-Googler John Giannandrea dives into the details with Ars.
Historically, Apple has not had a public reputation for leading in this area. That’s partially because people associate AI with digital assistants, and reviewers frequently call Siri less useful than Google Assistant or Amazon Alexa. And with ML, many tech enthusiasts say that more data means better models—but Apple is not known for data collection in the same way as, say, Google.
Despite this, Apple has included dedicated hardware for machine learning tasks in most of the devices it ships. Read More
Explainable AI: A guide for making black box machine learning models explainable
In the future, AI will explain itself, and interpretability could boost machine intelligence research. Getting started with the basics is a good way to get there, and Christoph Molnar’s book is a good place to start.
Christoph Molnar is a data scientist and PhD candidate in interpretable machine learning. Molnar has written the book “Interpretable Machine Learning: A Guide for Making Black Box Models Explainable”, in which he elaborates on the issue and examines methods for achieving explainability. Read More
Black Hat 2020: Open-Source AI to Spur Wave of ‘Synthetic Media’ Attacks
The explosion of open-source AI models are lowering the barrier of entry for bad actors to create fake video, audio and images – and Facebook, Twitter and other platforms aren’t ready.
An abundance of deep-learning and open-source technologies are making it easy for cybercriminals to generate fake images, text and audio called “synthetic media”. This type of media can be easily leveraged on Facebook, Twitter and other social media platforms to launch disinformation campaigns with hijacked identities.
At a Wednesday session at Black Hat USA 2020, researchers with FireEye demonstrated how freely-available, open-source tools – which offer pre-trained natural language processing, computer vision, and speech recognition tools – can be used to create malicious the synthetic media. Read More
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