Today, machine learning (ML) is coming into its own, ready to serve mankind in a diverse array of applications – from highly efficient manufacturing, medicine and massive information analysis to self-driving transportation, and beyond. However, if misapplied, misused or subverted, ML holds the potential for great harm – this is the double-edged sword of machine learning. "Over the last decade, researchers have focused on realizing practical ML capable of accomplishing real-world tasks and making them more efficient," said Dr. Hava Siegelmann, program manager in DARPA's Information Innovation Office (I2O). But, in a very real way, we've rushed ahead, paying little attention to vulnerabilities inherent in ML platforms – particularly in terms of altering, corrupting or deceiving these systems." In a commonly cited example, ML used by a self-driving car was tricked by visual alterations to a stop sign. While a human viewing the altered sign would have no difficulty interpreting its meaning, the ML erroneously interpreted the stop sign as a 45 mph speed limit posting. In a real-world attack like this, the self-driving car would accelerate through the stop sign, potentially causing a disastrous outcome. This is just one of many recently discovered attacks applicable to virtually any ML application. To get ahead of this acute safety challenge, DARPA created the Guaranteeing AI Robustness against Deception (GARD) program. GARD aims to develop a new generation of defenses against adversarial deception attacks on ML models. Current defense efforts were designed to protect against specific, pre-defined adversarial attacks and, remained vulnerable to attacks outside their design parameters when tested. GARD seeks to approach ML defense differently – by developing broad-based defenses that address the numerous possible attacks in a given scenario. "There is a critical need for ML defense as the technology is increasingly incorporated into some of our most critical infrastructure.

Former Bush senior adviser Karl Rove and Fox News correspondent-at-large Geraldo Rivera weigh in on the State of the Union's impact on the Trump presidency. Where was President Donald Trump during Tuesday's State of the Union address? The racist, sexist, immigrant-hating, cruel, anti-Semitic, divisive monster who is denounced continuously by his Democrat/media/Left critics was AWOL. He was sequestered in an undisclosed location and replaced with an advanced animatronic device. Unlike the Trump whose dark portrait the Left paints daily, this amazingly lifelike invention offered stirring words that should soothe blacks, women, immigrants, the infirm, Jews, and millions of Americans who desire national unity.

But the ambiguity of human decision-making often makes it extraordinarily hard for the legal system to know whether anyone has actually discriminated. To understand how algorithms affect discrimination, we must therefore also understand how they affect the problem of detecting discrimination. By one measure, algorithms are fundamentally opaque, not just cognitively but even mathematically. Yet for the task of proving discrimination, processes involving algorithms can provide crucial forms of transparency that are otherwise unavailable. These benefits do not happen automatically. But with appropriate requirements in place, the use of algorithms will make it possible to more easily examine and interrogate the entire decision process, thereby making it far easier to know whether discrimination has occurred. By forcing a new level of specificity, the use of algorithms also highlights, and makes transparent, central tradeoffs among competing values. Algorithms are not only a threat to be regulated; with the right safeguards in place, they have the potential to be a positive force for equity.

In this paper, we investigate the use of global information to speed up the learning process and increase the cumulative rewards of multi-agent reinforcement learning (MARL) tasks. Within the actor-critic MARL, we introduce multiple cooperative critics from two levels of the hierarchy and propose a hierarchical critic-based MARL algorithm. In our approach, the agent is allowed to receive information from local and global critics in a competition task. The agent not only receives low-level details but also considers coordination from high levels to obtain global information for increasing operational performance. Here, we define multiple cooperative critics in a top-down hierarchy, called the Hierarchical Critic Assignment (HCA) framework. Our experiment, a two-player tennis competition task performed in the Unity environment, tested the HCA multi-agent framework based on the Asynchronous Advantage Actor-Critic (A3C) with Proximal Policy Optimization (PPO) algorithm. The results showed that the HCA framework outperforms the non-hierarchical critic baseline method on MARL tasks.

Little by little, newspapers are revealing the bright future that Artificial Intelligence (AI) is building. Intelligent machines will help everywhere. However, this bright future may have a possible dark side: a dramatic job market contraction before its unpredictable transformation. Hence, in a near future, large numbers of job seekers may need financial support while catching up with these novel unpredictable jobs. This possible job market crisis has an antidote inside. In fact, the rise of AI is sustained by the biggest knowledge theft of the recent years. Many learning AI machines are extracting knowledge from unaware skilled or unskilled workers by analyzing their interactions. By passionately doing their jobs, many of these workers are shooting themselves in the feet. In this paper, we propose Human-in-the-loop Artificial Intelligence (HitAI) as a fairer paradigm for AI systems. Recognizing that any AI system has humans in the loop, HitAI will reward these aware and unaware knowledge producers with a different scheme: decisions of AI systems generating revenues will repay the legitimate owners of the knowledge used for taking those decisions. As modern Merry Men, HitAI researchers should fight for a fairer Robin Hood Artificial Intelligence that gives back what it steals. This article is part of the special track on AI and Society.

We introduce EvalAI, an open source platform for evaluating and comparing machine learning (ML) and artificial intelligence algorithms (AI) at scale. EvalAI is built to provide a scalable solution to the research community to fulfill the critical need of evaluating machine learning models and agents acting in an environment against annotations or with a human-in-the-loop. This will help researchers, students, and data scientists to create, collaborate, and participate in AI challenges organized around the globe. By simplifying and standardizing the process of benchmarking these models, EvalAI seeks to lower the barrier to entry for participating in the global scientific effort to push the frontiers of machine learning and artificial intelligence, thereby increasing the rate of measurable progress in this domain. Our code is available here.

Republican Rep. Mike Henderson introduced a bill that would make it a misdemeanor for anyone to knowingly fly a drone within 300 vertical feet (90 meters) and near the furthest perimeter of prisons, the St. The legislation would also add felony charges for attempting to drop contraband into state prison grounds.

Dana Deasy, chief information officer at the Department of Defense, announced on Monday the launch of a new strategy that aims to migrate storage and computing operations to the cloud. The DoD Cloud Strategy "addresses what we're trying to do, … the problems we're trying to solve and the objectives [we want to meet]," Deasy said in a statement published Monday. Deasy, a 2019 Wash100 winner, discussed how the new initiative will help advance the adoption of the Joint Enterprise Defense Infrastructure cloud platform, ensure cybersecurity and implement digital modernization efforts, including the development of artificial intelligence-based applications. "By having an enterprise cloud, it's what we put on top of it -- in this case, artificial intelligence -- [so] it's almost a fundamental imperative to have a cloud in place to do great things with AI," he said. Dana Deasy will be featured as a keynote speaker during Potomac Officers Club's upcoming Artificial Intelligence Summit on February 13th at the Hilton-McLean in McLean, Va.

Dimension 404 on Hulu is a science fiction anthology show in the tradition of The Twilight Zone and The Outer Limits. TV writer Andrea Kail loved the fifth episode, "Bob," about a (literal) giant brain who works for the National Security Agency. "I thought this was one of the best things I've seen in a long time," Kail says in Episode 347 of the Geek's Guide to the Galaxy podcast. "I thought it was incredibly good filmmaking, and incredibly great writing and acting. There was nothing about it I didn't love."

Many believed an algorithm would transcend humanity with cognitive awareness. Machines would discern and learn tasks without human intervention and replace workers in droves. They quite literally would be able to "think". Many people even raised the question whether we could have robots for spouses. But I am not talking about today.