Conventional seq2seq chatbot models attempt only to find sentences with the highest probabilities conditioned on the input sequences, without considering the sentiment of the output sentences. In this paper, we investigate four models to scale or adjust the sentiment of the chatbot response: a persona-based model, reinforcement learning, a plug and play model, and CycleGAN, all based on the seq2seq model. We also develop machine-evaluated metrics to estimate whether the responses are reasonable given the input. These metrics, together with human evaluation, are used to analyze the performance of the four models in terms of different aspects; reinforcement learning and CycleGAN are shown to be very attractive.

Autonomous and semi-autonomous systems for safety-critical applications require rigorous testing before deployment. Due to the complexity of these systems, formal verification may be impossible and real-world testing may be dangerous during development. Therefore, simulation-based techniques have been developed that treat the system under test as a black box during testing. Safety validation tasks include finding disturbances to the system that cause it to fail (falsification), finding the most-likely failure, and estimating the probability that the system fails. Motivated by the prevalence of safety-critical artificial intelligence, this work provides a survey of state-of-the-art safety validation techniques with a focus on applied algorithms and their modifications for the safety validation problem. We present and discuss algorithms in the domains of optimization, path planning, reinforcement learning, and importance sampling. Problem decomposition techniques are presented to help scale algorithms to large state spaces, and a brief overview of safety-critical applications is given, including autonomous vehicles and aircraft collision avoidance systems. Finally, we present a survey of existing academic and commercially available safety validation tools.

Inferring objects and their relationships from an image is useful in many applications at the intersection of vision and language. Due to a long tail data distribution, the task is challenging, with the inevitable appearance of zero-shot compositions of objects and relationships at test time. Current models often fail to properly understand a scene in such cases, as during training they only observe a tiny fraction of the distribution corresponding to the most frequent compositions. This motivates us to study whether increasing the diversity of the training distribution, by generating replacement for parts of real scene graphs, can lead to better generalization? We employ generative adversarial networks (GANs) conditioned on scene graphs to generate augmented visual features. To increase their diversity, we propose several strategies to perturb the conditioning. One of them is to use a language model, such as BERT, to synthesize plausible yet still unlikely scene graphs. By evaluating our model on Visual Genome, we obtain both positive and negative results. This prompts us to make several observations that can potentially lead to further improvements.

In 2018, Congress established the National Security Commission on Artificial Intelligence (NSCAI)--a temporary, independent body tasked with reviewing the national security implications of artificial intelligence (AI). But two years later, the commission's activities remain little known to the public. Critics have charged that the commission has conducted activities of interest to the public outside of the public eye, only acknowledging that meetings occurred after the fact and offering few details on evolving commission decision-making. As one commentator remarked, "Companies or members of the public interested in learning how the Commission is studying AI are left only with the knowledge that appointed people met to discuss these very topics, did so, and are not yet releasing any information about their recommendations." That perceived lack of transparency may soon change.

A false facial recognition match has led to the arrest of another innocent person. According to the Detroit Free Press, police in the city arrested a man for allegedly reaching into a person's car, taking their phone and throwing it, breaking the case and damaging the screen in the process. Facial recognition flagged Michael Oliver as a possible suspect, and the victim identified him in a photo lineup as the person who damaged their phone. Oliver was charged with a felony count of larceny over the May 2019 incident. He said he didn't commit the crime and the evidence supported his claim.

The high-profile case of a Black man wrongly arrested this year wasn't the first misidentification linked to controversial facial recognition technology used by Detroit police, the Free Press has learned. Last year, a 25-year-old Detroit man was wrongly accused of a felony for supposedly reaching into a teacher's vehicle, grabbing a cellphone and throwing it, cracking the screen and breaking the case. Detroit police used facial recognition technology in that investigation, too. It identified Michael Oliver as an investigative lead. After that hit, the teacher whose phone was snatched from his hands identified Oliver in a photo lineup as the person responsible.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. What if a U.S. drone was closely tracking an armed enemy vehicle as it transits rough terrain, enters urban areas and comes closer to vulnerable target areas when, all of a sudden, the target leaves a sensor's field of view, becoming seemingly un-trackable? Not so fast, according to emerging AI-enabled tracking technology now being developed by CACI, a technology firm supporting the U.S. military. Fast-maturing algorithms are now able to analyze a host of variables at one time, at lightning speed, to discern a target's trajectory and continue tracking an object even after it has left a sensor's field of view.

Earlier this week, the President's Council of Advisors on Science and Technology (PCAST) released a report outlining what it believes must happen for the U.S. to advance "industries of the future." Several of the committee's suggestions touched on the field of AI as it relates to federal, state, and private-sector partnerships, as well as departmental budgetary considerations. In particular, the report recommends that the U.S. grow nondefense federal investments in AI by 10 times over the next 10 years and for the federal government to create national AI "testbeds," expanding the National Science Foundation's (NSF) AI Institutes with at least one AI Institute in each state and creating a "National AI Consortia" to share capabilities, data, and resources. Loosely, PCAST -- which lives in the Office of Science and Technology -- provides advice to the president on science and technology policy. In the report, the committee argues the U.S. will need to boost AI R&D investments from $1 billion a year in 2020 to $10 billion a year by 2030 in order to remain competitive.

As NASA gears up to send humans to the moon and Mars it is also working on new advances to protect the space terrains from human germs. The American space agency released updates to its Planetary Protection Policies that provide new requirements for both astronaut and robotic missions. The added policies note that no biological matter is left on or around the moon, along with humans are to not contaminate any part of Mars with biological materials or return to Earth with germs from the Red Planet. The first woman and next man are set to head to the moon in 2024 and the first crewed mission to Mars is planned for the 2030s – and as early as 2035. The added policies note that no biological matter is left on or around the moon.

The high-profile case of a Black man wrongly arrested earlier this year wasn't the first misidentification linked to controversial facial recognition technology used by Detroit police, the Free Press has learned. Last year, a 25-year-old Detroit man was wrongly accused of a felony for supposedly reaching into a teacher's vehicle, grabbing a cell phone and throwing it, cracking the screen and breaking the case. Detroit police used facial recognition technology in that investigation, too. It identified Michael Oliver as an investigative lead. After that hit, the teacher who had his phone snatched from his hands identified Oliver in a photo lineup as the person responsible.